Processors

Amides

The Amides processor implements the proof-of-concept Adaptive Misuse Detection System (AMIDES). AMIDES extends conventional rule matching of SIEM systems by applying machine learning components aiming to detect attacks that evade existing SIEM rules as well as otherwise undetected attack variants. It learns from SIEM rules and historical benign events and can thus estimate which SIEM rule was tried to be evaded. An overview of AMIDES is depicted in the figure below.

The machine learning components of AMIDES are trained using the current SIEM rule set and historical benign events. Incoming events are transformed into feature vectors by the feature extraction component. During operation, features learned during the training phase will be re-used by the feature extraction component. Feature vectors are then passed to the Misuse Classification component which classifies events as malicious or benign. In case of a malicious result, the feature vector is passed to the Rule Attribution component which generates a ranked list of SIEM rules potentially evaded by the event. Finally, results generated by the Rule Attribution component and conventional rule matching results can be correlated for alert generation.

Since there is a plethora of different SIEM event types, the current implementation focuses on events that provide process command lines. Command lines are most commonly targeted by SIEM rules while they are also highly vulnerable to evasions. The rules and models for AMIDES provided in the deployment examples are for Sysmon Process Creation events. In general, the Amides rule format allows to create rules for other event types that provide process command lines, e.g. Process Creation events generated by Windows Security Auditing.

Misuse classification is performed by the MisuseDetector class. Instances of the MisuseDetector contain the model for misuse classification, which includes the trained classifier instance, the corresponding feature extractor, and an additional scaler to transform classifier results into the pre-defined output range between 0 and 1. The processor configuration parameter decision_threshold is used to fine-tune the classification results produced by the misuse detector.

Rule attribution is performed by the RuleAttributor class. The num_rule_attributions configuration parameter determines the number of rule attributions returned by the attributor. Models and vectorizer for rule attribution and feature extraction are held by RuleAttributor instances.

In order to speed up the detection and attribution process, the Amides processor makes use of a LRU cache that keeps track of incoming command line samples. In case of a previously seen command line, classification and attribution results can be retrieved from the cache in a shorter amount of time. The max_cache_entries configuration parameter determines the maximum number of elements of the internal cache.

Models used by the MisuseDetector and RuleAttributor are currently generated by scikit-learn. Each trained model needs to be packed into a dictionary together with its corresponding feature extractor and scaler. Dictionaries are then pickled and compressed (.zip). The URI or path of the compressed models file is given by the models_path configuration parameter. An example of a configuration of the Amides processor is given below:

Processor Configuration

- amides:
    type: amides
    rules:
        - tests/testdata/rules/rules
    max_cache_entries: 10000
    decision_threshold: 0.0
    num_rule_attributions: 10

To keep track of the components performance, the Amides processor tracks several processor metrics. This includes the mean misuse detection time, the mean rule attribution time, and several cache-related metrics like the number of hits and misses and the current cache load.

class logprep.processor.amides.processor.Amides.Config

Amides processor configuration class.

max_cache_entries: int: Maximum number of cached command lines and their rule attribution results.

decision_threshold: float: Specifies the decision threshold of the misuse detector to adjust it’s overall classification performance.

num_rule_attributions: int: Number of rule attributions returned in case of a positive misuse detection result.

models_path: str: Path or URI of the archive (.zip) containing the models used by the misuse detector and the rule attributor.

Security Best Practice - Processor - Amides Model

Ensure that you only use models from trusted sources, as it can be used to inject python code into the runtime.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

This rule enables to check if incoming documents are of a specific type suitable for classification by the Amides processor. The specified source_field should contain command line strings. In case of an positive detection result, rule attributions are written into the target_field.

The following example shows a complete rule:

Example

filter: 'some_field: "sample_cmdline"'
amides:
    source_fields: ["process.command_line"]
    target_field: "rule_attributions"
description: Sample rule for AMIDES processor.

class logprep.processor.amides.rule.AmidesRule.Config

Config of AmidesRule to specify source fields of command lines and target field of rule attribution results.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Calculator

The Calculator can be used to calculate with or without field values.

Processor Configuration

- calculatorname:
    type: calculator
    rules:
        - tests/testdata/rules/rules

class logprep.processor.calculator.processor.Calculator.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given calculator rule

filter: 'duration'
calculator:
  target_field: duration
  calc: ${duration} * 10e5
  overwrite_target: True
description: '...'

Incoming event

{"duration": "0.01"}

Processed event

{"duration": 10000.0}

class logprep.processor.calculator.rule.CalculatorRule.Config

Config for Calculator

calc: str: The calculation expression. Fields from the event can be used by surrounding them with ${ and }.

timeout: int: The maximum time in seconds for the calculation. Defaults to 1

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged, and the event is not tagged with the a failure tag. As soon as one field is missing no calculation is performed at all. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Following a list with example calculation expressions, where all factors and the operators can be retrieved from a field with the schema ${your.dotted.field}:

9 => 9
-9 => -9
--9 => 9
-E => -math.e
9 + 3 + 6 => 9 + 3 + 6
9 + 3 / 11 => 9 + 3.0 / 11
(9 + 3) => (9 + 3)
(9+3) / 11 => (9 + 3.0) / 11
9 - 12 - 6 => 9 - 12 - 6
9 - (12 - 6) => 9 - (12 - 6)
2*3.14159 => 2 * 3.14159
3.1415926535*3.1415926535 / 10 => 3.1415926535 * 3.1415926535 / 10
PI * PI / 10 => math.pi * math.pi / 10
PI*PI/10 => math.pi * math.pi / 10
PI^2 => math.pi ** 2
round(PI^2) => round(math.pi ** 2)
6.02E23 * 8.048 => 6.02e23 * 8.048
e / 3 => math.e / 3
sin(PI/2) => math.sin(math.pi / 2)
10+sin(PI/4)^2 => 10 + math.sin(math.pi / 4) ** 2
trunc(E) => int(math.e)
trunc(-E) => int(-math.e)
round(E) => round(math.e)
round(-E) => round(-math.e)
E^PI => math.e ** math.pi
exp(0) => 1
exp(1) => math.e
2^3^2 => 2 ** 3 ** 2
(2^3)^2 => (2 ** 3) ** 2
2^3+2 => 2 ** 3 + 2
2^3+5 => 2 ** 3 + 5
2^9 => 2 ** 9
sgn(-2) => -1
sgn(0) => 0
sgn(0.1) => 1
round(E, 3) => round(math.e, 3)
round(PI^2, 3) => round(math.pi ** 2, 3)
sgn(cos(PI/4)) => 1
sgn(cos(PI/2)) => 0
sgn(cos(PI*3/4)) => -1
+(sgn(cos(PI/4))) => 1
-(sgn(cos(PI/4))) => -1
hypot(3, 4) => 5
multiply(3, 7) => 21
all(1,1,1) => True
all(1,1,1,1,1,0) => False

The calc expression is not whitespace sensitive.

Clusterer

The log clustering is mainly developed for Syslogs, unstructured and semi-structured logs. The clusterer calculates a log signature based on the message field. The log signature is calculated with heuristic and deterministic rules. The idea of a log signature is to extract a subset of the constant parts of a log and to delete the dynamic parts. If the fields syslog.facility and event.severity are in the log, then they are prefixed to the log signature.

Logs are only clustered if at least one of the following criteria is fulfilled:

Criteria 1: { "message": "A sample message", "tags": ["clusterable", ...], ... }
Criteria 2: { "message": "A sample message", "clusterable": true, ... }
Criteria 3: { "message": "A sample message", "syslog": { "facility": <number> }, "event": { "severity": <string> }, ... }

Processor Configuration

- clusterername:
    type: clusterer
    rules:
        - tests/testdata/rules/rules
    output_field_name: target_field

class logprep.processor.clusterer.processor.Clusterer.Config

Clusterer Configuration

output_field_name: str: defines in which field results of the clustering should be stored.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

Rules of the clusterer are evaluated in alphanumerical order. Some rules do only make sense if they are performed in a sequence with other rules. The clusterer matches multiple rules at once and applies them all before creating a clustering signature. Therefore, it is recommended to prefix rules with numbers, i.e. 00_01_*. Here the first two digits represent a type of rules that make sense together and the second digits represent the order of rules of the same type.

A subset of terms from this field can be extracted into the clustering-signature field defined in the clusterer configuration.

Since clusterer rules must be used in a sequence, it makes no sense to perform regular auto tests on them. Thus, every rule can have a field tests containing signature calculation tests. It can contain one test or a list of tests. Each tests consists of the fields tests.raw and tests.result. tests.raw is the input and would be usually the message. tests.result is the expected result.

Example - One Test

filter: ...
clusterer: ...
tests:
  raw:    'Some message'
  result: 'Some changed message'

Example - Multiple Test

filter: ...
clusterer: ...
tests:
  - raw:    'Some message'
    result: 'Some changed message'
  - raw:    'Another message'
    result: 'Another changed message'

In the following rule example the word byte is stemmed.

Example - Stemming Rule

filter: message
clusterer:
  target: message
  pattern: '(bytes|Bytes|Byte)'
  repl: 'byte'
description: '...'
tests:
  raw:    'Byte is a Bytes is a bytes is a byte'
  result: 'byte is a byte is a byte is a byte'

In the following rule example the word baz is removed.

Example - Removal Rule

filter: message
clusterer:
  target: message
  pattern: 'foo (bar) baz'
  repl: ''
description: '...'
tests:
  raw:    'foo bar baz'
  result: 'foo  baz'

In the following rule example the word baz is surrounded by extraction tags.

Example - Extraction Rule

filter: message
clusterer:
  target: message
  pattern: 'foo (bar) baz'
  repl: '<+></+>'
description: '...'
tests:
  raw:    'foo bar baz'
  result: 'foo <+>bar</+> baz'

class logprep.processor.clusterer.rule.ClustererRule.Config

RuleConfig for Clusterer

pattern: Pattern: Defines the regex pattern that will be matched on the clusterer.source_fields.

repl: str

Anything within a capture group in clusterer.pattern will be substituted with values defined in clusterer.repl. The clusterer will only extract terms into a signature that are surrounded by the tags <+></+>. One could first use rules to remove common terms, other rules to perform stemming and finally rules to wrap terms in <+></+> to create a signature.

For example: * Setting clusterer.repl: '' would remove anything within a capture group. * Setting clusterer.repl: 'FOO' would replace anything within a capture group with FOO. * Setting clusterer.repl: '<+></+>' would surround anything within a capture group with <+></+>.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to True

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The field from where to get the value which should be clustered.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Concatenator

The concatenator processor allows to concat a list of source fields into one new target field. The concat separator and the target field can be specified. Furthermore, it is possible to directly delete all given source fields, or to overwrite the specified target field.

Processor Configuration

- Concatenatorname:
    type: concatenator
    rules:
        - tests/testdata/rules/rules

class logprep.processor.concatenator.processor.Concatenator.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given concatenator rule

filter: 'date AND time'
concatenator:
  source_fields: ["date", "time"]
  target_field: timestamp
  separator: " "
  overwrite_target: True
  delete_source_fields: True
description: '...'

Incoming event

{
    "date": "01.01.1007",
    "time": "13:07"
}

Processed event

{
    "timetsamp": "01.01.1007 13:07"
}

class logprep.processor.concatenator.rule.ConcatenatorRule.Config

RuleConfig for Concatenator

separator: str: The character(s) that should be used between the combined source field values.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The source fields that should be concatenated, can contain dotted field paths.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

DatetimeExtractor

The datetime_extractor is a processor that can extract timestamps from a field and split it into its parts.

Processor Configuration

- datetimeextractorname:
    type: datetime_extractor
    rules:
        - tests/testdata/rules/rules

class logprep.processor.datetime_extractor.processor.DatetimeExtractor.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The datetime extractor requires the additional field datetime_extractor. The additional fields datetime_extractor.source_fields and datetime_extractor.target_field must be defined. The first one contains the name of the field from which the timestamp should be taken and the last one contains the name of the field under which a split timestamp should be written.

In the following example the timestamp will be extracted from @timestamp and written to split_@timestamp.

Example

filter: '@timestamp'
datetime_extractor:
  source_fields: ['@timestamp']
  target_field: 'split_@timestamp'
description: '...'

class logprep.processor.datetime_extractor.rule.DatetimeExtractorRule.Config

Config for DatetimeExtractorRule

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to.

tests: List[Dict[str, str]]: Custom tests for this rule.

Deleter

The deleter is a processor that removes an entire event from further pipeline processing.

Processor Configuration

- deletename:
    type: deleter
    rules:
        - tests/testdata/rules/rules

class logprep.processor.deleter.processor.Deleter.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The example below deletes the log message if the message field equals “foo”.

Example delete rule

filter: 'message: "foo"'
deleter:
    delete: true
description: '...'

class logprep.processor.deleter.rule.DeleterRule.Config

Config for DeleterRule

delete: bool: Delete or not

target_field

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

tests: List[Dict[str, str]]: Custom tests for this rule.

Dissector

The dissector is a processor that tokenizes incoming strings using defined patterns. The behavior is based of the logstash dissect filter plugin and has the same advantage that for the event processing no regular expressions are used. Additionally, it can be used to convert datatypes of given fields.

Processor Configuration

- dissectorname:
    type: dissector
    rules:
        - tests/testdata/rules/rules

class logprep.processor.dissector.processor.Dissector.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given dissector rule

filter: message
dissector:
    mapping:
        message: "%{}of %{extracted.message_float} and an int of %{extracted.message_int}"
    convert_datatype:
        extracted.message_int: "int"
        extracted.message_float: "float"
description: '...'

Incoming event

{"message": "This message has a float of 1.23 and an int of 1337"}

Processed event

{
    "message": "This message has a float of 1.23 and an int of 1337",
    "extracted": {"message_float": 1.23, "message_int": 1337},
}

Dissect Pattern Language

The dissect pattern describes the textual format of the source field.

Given a dissect pattern of %{field1} %{field2} the source field value will be dissected into everything before the first whitespace which would be written into the field field1 and everything after the first whitespace which would be written into the field field2.

The string between %{ and } is the desired target field. This can be declared in dotted field notation (e.g. %{target.subfield1.subfield2}). Every subfield between the first and the last subfield will be created if necessary.

By default, the target field will always be overwritten with the captured value. If you want to append to a preexisting target field value, as string or list, you have to use the + operator. If you want to use a prefix before the appended string use this notation +( ). In this example a whitespace would be added before the extracted string is added. If you want to use the symbols ( or ) as your separator, you have to escape with \` (e.g. :code:`+(\()).

If you want to remove unwanted padding characters around a dissected pattern you have to use the -(<char>) notation, while <char> can be any character similar to the +( ) notation. If for example you have a field like "[2022-11-04 10:00:00 AM ] - 127.0.0.1" and you want to extract the timestamp and the ip, you can use the dissect-pattern [%{time-( )}] - %{ip} to remove the unwanted spaces after the ‘AM’. This works independent of the number of spaces.

It is also possible to capture the target field name from the source field value with the notation %{?<your name for the reference>} (e.g. %{?key1}). In the same dissection pattern this can be referred to with the notation %{&<the reference>} (e.g. %{&key1}). References can be combined with the append operator. For examples see below.

Additionally an optional convert datatype can be provided after the key using | as separator to convert the value from string to int, float or bool. The conversion to bool is interpreted by meaning. (e.g. yes is translated to True). When removing padding characters at the same time then the conversion has to come after the padding character (e.g. %{field2-(#)|bool}).

If you want to reorder parts of a dissection you can give the order by adding /<position> to the dissect pattern. A valid example would be: %{time/1} %{+time/3} %{+time/2}. When removing padding characters at the same time then the position has to come after the padding character (e.g. %{time-(*)/2}).

class logprep.processor.dissector.rule.DissectorRule.Config

Config for Dissector

convert_datatype: dict: A mapping from source field and desired datatype [optional]. The datatypes could be float, int, bool, string

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A mapping from source fields to a dissect pattern [optional]. Dotted field notation is possible in key and in the dissect pattern.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for dissection and datatype conversion:

writes new fields with same separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{field2} %{field3} %{field4}'}}}
message: {'message': 'This is a message'}
processed: {'message': 'This is a message', 'field1': 'This', 'field2': 'is', 'field3': 'a', 'field4': 'message'}

writes new fields with different separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{field2}:%{field3} %{field4}'}}}
message: {'message': 'This is:a message'}
processed: {'message': 'This is:a message', 'field1': 'This', 'field2': 'is', 'field3': 'a', 'field4': 'message'}

writes new fields with long separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} is %{field3} %{field4}'}}}
message: {'message': 'This is a message'}
processed: {'message': 'This is a message', 'field1': 'This', 'field3': 'a', 'field4': 'message'}

writes new fields and appends to existing list:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} is %{field3} %{+field4}'}}}
message: {'message': 'This is a message', 'field4': ['preexisting']}
processed: {'message': 'This is a message', 'field1': 'This', 'field3': 'a', 'field4': ['preexisting', 'message']}

writes new fields and appends to existing empty list:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} is %{field3} %{+field4}'}}}
message: {'message': 'This is a message', 'field4': []}
processed: {'message': 'This is a message', 'field1': 'This', 'field3': 'a', 'field4': ['message']}

writes new fields and appends to existing string:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} is %{field3} %{+( )field4}'}}}
message: {'message': 'This is a message', 'field4': 'preexisting'}
processed: {'message': 'This is a message', 'field1': 'This', 'field3': 'a', 'field4': 'preexisting message'}

writes new dotted fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{my.new.field2} %{field3} %{+field4}'}}}
message: {'message': 'This is a message', 'field4': 'preexisting'}
processed: {'message': 'This is a message', 'field1': 'This', 'my': {'new': {'field2': 'is'}}, 'field3': 'a', 'field4': 'preexistingmessage'}

overwrites dotted fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{my.new.field2} %{field3} %{+( )field4}'}}}
message: {'message': 'This is a message', 'field4': 'preexisting', 'my': {'new': {'field2': 'preexisting'}}}
processed: {'message': 'This is a message', 'field1': 'This', 'my': {'new': {'field2': 'is'}}, 'field3': 'a', 'field4': 'preexisting message'}

appends to dotted fields preexisting string:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{+my.new.field2} %{field3} %{+( )field4}'}}}
message: {'message': 'This is a message', 'field4': 'preexisting', 'my': {'new': {'field2': 'preexisting'}}}
processed: {'message': 'This is a message', 'field1': 'This', 'my': {'new': {'field2': 'preexistingis'}}, 'field3': 'a', 'field4': 'preexisting message'}

appends to dotted fields preexisting list:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{+my.new.field2} %{field3} %{+( )field4}'}}}
message: {'message': 'This is a message', 'field4': 'preexisting', 'my': {'new': {'field2': ['preexisting']}}}
processed: {'message': 'This is a message', 'field1': 'This', 'my': {'new': {'field2': ['preexisting', 'is']}}, 'field3': 'a', 'field4': 'preexisting message'}

processes dotted source field:

rule: {'filter': 'message', 'dissector': {'mapping': {'message.key1.key2': '%{field1} %{field2} %{field3} %{field4}'}}}
message: {'message': {'key1': {'key2': 'This is the message'}}}
processed: {'message': {'key1': {'key2': 'This is the message'}}, 'field1': 'This', 'field2': 'is', 'field3': 'the', 'field4': 'message'}

processes multiple mappings to different target fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'source1': '%{extracted.source1.key1} %{extracted.source1.key2} %{extracted.source1.key3}', 'source2': '%{extracted.source2.key1} %{extracted.source2.key2} %{extracted.source2.key3}'}}}
message: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2'}
processed: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2', 'extracted': {'source1': {'key1': 'This', 'key2': 'is', 'key3': 'source1'}, 'source2': {'key1': 'This', 'key2': 'is', 'key3': 'source2'}}}

processes multiple mappings to same target fields (overwrite):

rule: {'filter': 'message', 'dissector': {'mapping': {'source1': '%{extracted.key1} %{extracted.key2} %{extracted.key3}', 'source2': '%{extracted.key1} %{extracted.key2} %{extracted.key3}'}}}
message: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2'}
processed: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2', 'extracted': {'key1': 'This', 'key2': 'is', 'key3': 'source2'}}

processes multiple mappings to same target fields (appending):

rule: {'filter': 'message', 'dissector': {'mapping': {'source1': '%{+extracted.key1} %{+extracted.key2} %{+extracted.key3}', 'source2': '%{+( )extracted.key1} %{+( )extracted.key2} %{+( )extracted.key3}'}}}
message: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2'}
processed: {'message': 'This message does not matter', 'source1': 'This is source1', 'source2': 'This is source2', 'extracted': {'key1': 'This This', 'key2': 'is is', 'key3': 'source1 source2'}}

append to new field in different order as string:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{+( )extracted/4} %{+( )extracted/3} %{+( )extracted/2} %{+extracted/1}'}}}
message: {'message': 'This is the message'}
processed: {'message': 'This is the message', 'extracted': 'message the is This'}

append to existing field in different order as string:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{+( )extracted/4} %{+( )extracted/3} %{+( )extracted/2} %{+( )extracted/1}'}}}
message: {'message': 'This is the message', 'extracted': 'preexisting'}
processed: {'message': 'This is the message', 'extracted': 'preexisting message the is This'}

append to existing empty list field in different order as list:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{+extracted/4} %{+extracted/3} %{+extracted/2} %{+extracted/1}'}}}
message: {'message': 'This is the message', 'extracted': []}
processed: {'message': 'This is the message', 'extracted': ['message', 'the', 'is', 'This']}

append to existing prefilled field in different order as list:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{+extracted/4} %{+extracted/3} %{+extracted/2} %{+extracted/1}'}}}
message: {'message': 'This is the message', 'extracted': ['preexisting']}
processed: {'message': 'This is the message', 'extracted': ['preexisting', 'message', 'the', 'is', 'This']}

append to new field in specified order as string with multiple fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{}: %{+( )extracted/2}', 'message2': '%{}: %{+extracted/1}'}}}
message: {'message': 'The first message: first', 'message2': 'The second message: second'}
processed: {'message': 'The first message: first', 'message2': 'The second message: second', 'extracted': 'second first'}

converts datatype without mapping:

rule: {'filter': 'message', 'dissector': {'convert_datatype': {'message': 'int'}}}
message: {'message': '42'}
processed: {'message': 42}

converts datatype with mapping in dotted field notation:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{}of %{extracted.message_float} and a int of %{extracted.message_int}'}, 'convert_datatype': {'extracted.message_int': 'int', 'extracted.message_float': 'float'}}}
message: {'message': 'This message has a float of 1.23 and a int of 1337'}
processed: {'message': 'This message has a float of 1.23 and a int of 1337', 'extracted': {'message_float': 1.23, 'message_int': 1337}}

indirect field notation: uses captured field as key:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{?key} %{&key}'}}}
message: {'message': 'This is the message'}
processed: {'message': 'This is the message', 'This': 'is the message'}

indirect field notation: uses captured field as key and appends to it:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{?key} %{&key} %{} %{+( )&key}'}}}
message: {'message': 'This is the message'}
processed: {'message': 'This is the message', 'This': 'is message'}

handles special chars as captured content:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{field2} %{field3} %{+field4}'}}}
message: {'message': 'This is \a + message'}
processed: {'message': 'This is \a + message', 'field1': 'This', 'field2': 'is', 'field3': '\a', 'field4': '+ message'}

handles special chars in captured content and target field names:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{~field1} %{fie ld2} %{$fie}ld3} %{+field4}'}}}
message: {'message': '&This isx02 a mess}age /1'}
processed: {'message': '&This isx02 a mess}age /1', '~field1': '&This', 'fie ld2': 'isx02', '$fie}ld3': 'a', 'field4': 'mess}age /1'}

deletes source fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{field1} %{field2} %{field3} %{field4}', 'message2': '%{field21} %{field22} %{field23} %{field24}'}, 'delete_source_fields': True}}
message: {'message': 'This is a message', 'message2': 'This is a message'}
processed: {'field1': 'This', 'field2': 'is', 'field3': 'a', 'field4': 'message', 'field21': 'This', 'field22': 'is', 'field23': 'a', 'field24': 'message'}

parses path elements:

rule: {'filter': 'path', 'dissector': {'mapping': {'path': '/%{field1}/%{field2}/%{field3}/%{field4}'}}}
message: {'path': '/this/is/the/path'}
processed: {'path': '/this/is/the/path', 'field1': 'this', 'field2': 'is', 'field3': 'the', 'field4': 'path'}

Appending without separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'INFO#%{date}#%{+date}#MOREINFO%{}'}}}
message: {'message': 'INFO#2022 12 06 15:12:30:534#+0100#MOREINFO'}
processed: {'message': 'INFO#2022 12 06 15:12:30:534#+0100#MOREINFO', 'date': '2022 12 06 15:12:30:534+0100'}

Appending with special field separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'INFO#%{+($)date}#%{+($)date}#MOREINFO%{}'}}}
message: {'message': 'INFO#2022 12 06 15:12:30:534#+0100#MOREINFO'}
processed: {'message': 'INFO#2022 12 06 15:12:30:534#+0100#MOREINFO', 'date': '(2022 12 06 15:12:30:534)+0100'}

Dissection with delimiter ending:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'this is %{target}.'}}}
message: {'message': 'this is the message.'}
processed: {'message': 'this is the message.', 'target': 'the message'}

Convert datatype via dissect pattern:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'this is %{field1|int} message and this is %{field2|bool}'}}}
message: {'message': 'this is 42 message and this is 0'}
processed: {'message': 'this is 42 message and this is 0', 'field1': 42, 'field2': False}

Strip char after dissecting:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time-( )}] - %{ip}'}}}
message: {'message': '[2022-11-04 10:00:00 AM ] - 127.0.0.1'}
processed: {'message': '[2022-11-04 10:00:00 AM ] - 127.0.0.1', 'time': '2022-11-04 10:00:00 AM', 'ip': '127.0.0.1'}

Strip special char after dissecting:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time-(#)}] - %{ip}'}}}
message: {'message': '[2022-11-04 10:00:00 AM####] - 127.0.0.1'}
processed: {'message': '[2022-11-04 10:00:00 AM####] - 127.0.0.1', 'time': '2022-11-04 10:00:00 AM', 'ip': '127.0.0.1'}

Strip another special char after dissecting:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time-(?)}] - %{ip}'}}}
message: {'message': '[2022-11-04 10:00:00 AM?????] - 127.0.0.1'}
processed: {'message': '[2022-11-04 10:00:00 AM?????] - 127.0.0.1', 'time': '2022-11-04 10:00:00 AM', 'ip': '127.0.0.1'}

Strip char on both sides:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time-(*)}] - %{ip}'}}}
message: {'message': '[***2022-11-04 10:00:00 AM***] - 127.0.0.1'}
processed: {'message': '[***2022-11-04 10:00:00 AM***] - 127.0.0.1', 'time': '2022-11-04 10:00:00 AM', 'ip': '127.0.0.1'}

Strip char while appending:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time} %{+( )time} %{+( )time-(*)}] - %{ip}'}}}
message: {'message': '[2022-11-04 10:00:00 AM***] - 127.0.0.1'}
processed: {'message': '[2022-11-04 10:00:00 AM***] - 127.0.0.1', 'time': '2022-11-04 10:00:00 AM', 'ip': '127.0.0.1'}

Strip char while changing position:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '[%{time/1} %{+( )time/3} %{+( )time-(*)/2}] - %{ip}'}}}
message: {'message': '[2022-11-04 10:00:00 AM***] - 127.0.0.1'}
processed: {'message': '[2022-11-04 10:00:00 AM***] - 127.0.0.1', 'time': '2022-11-04 AM 10:00:00', 'ip': '127.0.0.1'}

Strip char in indirect field notation:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{?key} %{&key-(#)} %{} %{+( )&key-(#)}'}}}
message: {'message': 'This is## the message####'}
processed: {'message': 'This is## the message####', 'This': 'is message'}

Strip char while inferring datatype:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'this is %{field1-(#)|int} message and this is %{field2-(#)|bool}'}}}
message: {'message': 'this is 42#### message and this is 0##'}
processed: {'message': 'this is 42#### message and this is 0##', 'field1': 42, 'field2': False}

extract end of string:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': 'system_%{type}'}}}
message: {'message': 'system_monitor'}
processed: {'message': 'system_monitor', 'type': 'monitor'}

copy field - dissect without separator:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{sys_type}'}}}
message: {'message': 'system_monitor'}
processed: {'message': 'system_monitor', 'sys_type': 'system_monitor'}

ignore missing fields:

rule: {'filter': 'message', 'dissector': {'mapping': {'message': '%{sys_type}', 'does_not_exist': '%{sys_type}'}, 'ignore_missing_fields': True}}
message: {'message': 'system_monitor'}
processed: {'message': 'system_monitor', 'sys_type': 'system_monitor'}

DomainLabelExtractor

The domain_label_extractor is a processor that splits a domain into it’s corresponding labels like registered_domain, top_level_domain and subdomain. If instead an IP is given in the target field an informational tag is added to the configured tags field. If neither a domain nor an ip address can be recognized an invalid error tag will be added to the tag field in the event. The added tags contain each the target field name that was checked by the configured rule, such that it is possible to distinguish between different domain fields in one event. For example for the target field url.domain following tags could be added: invalid_domain_in_url_domain and ip_in_url_domain

Processor Configuration

- domainlabelextractorname:
    type: domain_label_extractor
    rules:
        - tests/testdata/rules/rules
    tagging_field_name: resolved

class logprep.processor.domain_label_extractor.processor.DomainLabelExtractor.Config

DomainLabelExtractor config

tagging_field_name: str: Optional configuration field that defines into which field in the event the informational tags should be written to. If this field is not present it defaults to tags.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The domain label extractor requires the additional field domain_label_extractor. The mandatory keys under domain_label_extractor are source_fields and target_field. Former is used to identify the field (declared as list with one element) which contains the domain. And the latter is used to define the parent field where theresults should be written to. Both fields can be dotted subfields. The sub fields of the parent output field of the result are: registered_domain, top_level_domain and subdomain.

In the following example the domain www.sub.domain.de will be split into it’s subdomain www.sub, it’s registered domain domain and lastly it’s TLD de:

Example Rule to extract the labels / parts of a domain.

filter: 'url'
domain_label_extractor:
  source_fields: ['url.domain']
  target_field: 'url'
description: '...'

The example rule applied to the input event

Input Event

{
    "url": {
        "domain": "www.sub.domain.de"
    }
}

will result in the following output

Output Event

{
    "url": {
        "domain": "www.sub.domain.de",
        "registered_domain": "domain.de",
        "top_level_domain": "de",
        "subdomain": "www.sub"
    }
}

class logprep.processor.domain_label_extractor.rule.DomainLabelExtractorRule.Config

Config for DomainLabelExtractorRule

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

DomainResolver

The domain_resolver is a processor that can resolve domains inside a defined field.

Processor Configuration

- domainresolvername:
    type: domain_resolver
    rules:
        - tests/testdata/rules/rules
    timeout: 0.5
    max_cached_domains: 20000
    max_caching_days: 1
    hash_salt: secure_salt
    cache_enabled: true
    debug_cache: false

class logprep.processor.domain_resolver.processor.DomainResolver.Config

DomainResolver config

timeout: float | None: Timeout for resolving of domains.

max_cached_domains: int: The maximum number of cached domains. One cache entry requires ~250 Byte, thus 10 million elements would require about 2.3 GB RAM. The cache is not persisted. Restarting Logprep does therefore clear the cache.

max_caching_days: int: Number of days a domains is cached after the last time it appeared. This caching reduces the CPU load of Logprep (no demanding encryption must be performed repeatedly) and the load on subsequent components (i.e. Logstash or Opensearch). Setting the caching days to Null deactivates the caching. In case the cache size has been exceeded (see domain_resolver.max_cached_domains),the oldest cached pseudonyms will be discarded first.Thus, it is possible that a domain is re-added to the cache before max_caching_days has elapsed if it was discarded due to the size limit.

hash_salt: str: A salt that is used for hashing.

cache_enabled: bool: If enabled activates a cache such that already seen domains do not need to be resolved again.

debug_cache: bool: If enabled adds debug information to the current event, for example if the event was retrieved from the cache or newly resolved, as well as the cache size.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The domain resolver requires the additional field domain_resolver. The additional field domain_resolver.source_fields must be defined as list with one element. It contains the field from which an URL should be parsed and then written to resolved_ip. The URL can be located in continuous text insofar the URL is valid.

Optionally, the output field can be configured (overriding the default resolved_ip) using the parameter target_field. This can be a dotted subfield.

In the following example the URL from the field url will be extracted and written to resolved_ip.

Example

  filter: url
  domain_resolver:
    source_fields: [url]
  description: '...'

class logprep.processor.domain_resolver.rule.DomainResolverRule.Config

RuleConfig for DomainResolver

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processor output to. Defaults to resovled_ip

tests: List[Dict[str, str]]: Custom tests for this rule.

Dropper

The dropper is a processor that removes fields from log messages. Which fields are deleted is determined within each rule.

Processor Configuration

- droppername:
    type: dropper
    rules:
        - tests/testdata/rules/rules

class logprep.processor.dropper.processor.Dropper.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

Which fields are removed is defined in the additional field drop. It contains a list of fields in dot notation. For nested fields all subfields are also removed if they are empty. If only the specified subfield should be removed, then this can be achieved by setting the option drop_full: false.

In the following example the field keep_me.drop_me is deleted while the fields keep_me and keep_me.keep_me_too are kept.

Example - Rule

filter: keep_me.drop_me
dropper:
    drop:
    - keep_me.drop_me

Example - Input document

[{
    "keep_me": {
        "drop_me": "something",
        "keep_me_too": "something"
    }
}]

Example - Expected output after application of the rule

[{
    "keep_me": {
        "keep_me_too": "something"
    }
}]

class logprep.processor.dropper.rule.DropperRule.Config

RuleConfig for DropperRule

drop: list: List of fields to drop

drop_full: bool: Drop recursive? defaults to [True]

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

tests: List[Dict[str, str]]: Custom tests for this rule.

FieldManager

The field_manager processor copies or moves values from multiple source fields to one target field. Additionally, it can be used to merge multiple source field values into one target field value. In this process, source field lists will be merged.

Processor Configuration

- fieldmanagername:
    type: field_manager
    rules:
        - tests/testdata/rules/rules

class logprep.processor.field_manager.processor.FieldManager.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given field_manager rule

filter: client.ip
field_manager:
    source_fields:
        - client.ip
        - destination.ip
        - host.ip
        - observer.ip
        - server.ip
        - source.ip
        - server.nat.ip
        - client.nat.ip
    target_field: related.ip
    merge_with_target: True
description: '...'

Incoming event

{
    "client": {"ip": ["127.0.0.1", "fe89::", "192.168.5.1"], "nat": {"ip": "223.2.3.2"}},
    "destination": {"ip": "8.8.8.8"},
    "host": {"ip": ["192.168.5.1", "180.22.66.3"]},
    "observer": {"ip": "10.10.2.33"},
    "server": {"ip": "10.10.2.33", "nat": {"ip": "180.22.66.1"}},
    "source": {"ip": "10.10.2.33"}
}

Processed event

{
    "client": {"ip": ["127.0.0.1", "fe89::", "192.168.5.1"], "nat": {"ip": "223.2.3.2"}},
    "destination": {"ip": "8.8.8.8"},
    "host": {"ip": ["192.168.5.1", "180.22.66.3"]},
    "observer": {"ip": "10.10.2.33"},
    "server": {"ip": "10.10.2.33", "nat": {"ip": "180.22.66.1"}},
    "source": {"ip": "10.10.2.33"},
    "related": {
        "ip": [
            "10.10.2.33",
            "127.0.0.1",
            "180.22.66.1",
            "180.22.66.3",
            "192.168.5.1",
            "223.2.3.2",
            "8.8.8.8",
            "fe89::"
        ]
    }
}

class logprep.processor.field_manager.rule.FieldManagerRule.Config

Config for FieldManagerRule

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for field_manager:

copies single field to non existing target field:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field'}}
message: {'message': 'This is a message'}
processed: {'message': 'This is a message', 'new_field': 'This is a message'}

copies single field to existing target field:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field', 'overwrite_target': True}}
message: {'message': 'This is a message', 'new_field': 'existing value'}
processed: {'message': 'This is a message', 'new_field': 'This is a message'}

moves single field to non existing target field:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field', 'delete_source_fields': True}}
message: {'message': 'This is a message'}
processed: {'new_field': 'This is a message'}

moves single field to existing target field:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field', 'delete_source_fields': True, 'overwrite_target': True}}
message: {'message': 'This is a message', 'new_field': 'existing content'}
processed: {'new_field': 'This is a message'}

moves field and writes as list to target field:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'message': 'This is a message'}
processed: {'new_field': ['This is a message']}

moves multiple fields and writes them as list to non existing target field:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': 'value1', 'field2': 'value2', 'field3': 'value3'}
processed: {'new_field': ['value1', 'value2', 'value3']}

moves multiple fields and writes them as list to existing target field:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True, 'overwrite_target': True}}
message: {'field1': 'value1', 'field2': 'value2', 'field3': 'value3', 'new_field': 'i exist'}
processed: {'new_field': ['value1', 'value2', 'value3']}

moves multiple fields and replaces existing target field with list including the existing value:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': 'value1', 'field2': 'value2', 'field3': 'value3', 'new_field': 'i exist'}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3']}

moves multiple fields and writes them to a existing list:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': 'value1', 'field2': 'value2', 'field3': 'value3', 'new_field': ['i exist']}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3']}

moves multiple fields and writes them to a existing target field as list:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': 'value1', 'field2': 'value2', 'field3': 'value3', 'new_field': 'i exist'}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3']}

moves multiple fields and merges to target list:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': ['value1', 'value2', 'value3'], 'field2': ['value4'], 'field3': ['value5', 'value6'], 'new_field': ['i exist']}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3', 'value4', 'value5', 'value6']}

moves multiple fields and merges to target list with different source types:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': ['value1', 'value2', 'value3'], 'field2': 'value4', 'field3': ['value5', 'value6'], 'new_field': ['i exist']}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3', 'value4', 'value5', 'value6']}

(‘moves multiple fields and merges to target list ‘, ‘with different source types and filters duplicates’):

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True}}
message: {'field1': ['value1', 'value2', 'value3', 'value5'], 'field2': 'value4', 'field3': ['value5', 'value6', 'value4'], 'new_field': ['i exist']}
processed: {'new_field': ['i exist', 'value1', 'value2', 'value3', 'value5', 'value4', 'value6']}

(‘moves multiple fields and merges to target list ‘, ‘with different source types and filters duplicates and overwrites target’):

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True, 'delete_source_fields': True, 'overwrite_target': True}}
message: {'field1': ['value1', 'value2', 'value3', 'value5'], 'field2': 'value4', 'field3': ['value5', 'value6', 'value4'], 'new_field': ['i exist']}
processed: {'new_field': ['value1', 'value2', 'value3', 'value5', 'value4', 'value6']}

real world example from documentation:

rule: {'filter': 'client.ip', 'field_manager': {'source_fields': ['client.ip', 'destination.ip', 'host.ip', 'observer.ip', 'server.ip', 'source.ip', 'server.nat.ip', 'client.nat.ip'], 'target_field': 'related.ip', 'merge_with_target': True}}
message: {'client': {'ip': ['127.0.0.1', 'fe89::', '192.168.5.1'], 'nat': {'ip': '223.2.3.2'}}, 'destination': {'ip': '8.8.8.8'}, 'host': {'ip': ['192.168.5.1', '180.22.66.3']}, 'observer': {'ip': '10.10.2.33'}, 'server': {'ip': '10.10.2.33', 'nat': {'ip': '180.22.66.1'}}, 'source': {'ip': '10.10.2.33'}}
processed: {'client': {'ip': ['127.0.0.1', 'fe89::', '192.168.5.1'], 'nat': {'ip': '223.2.3.2'}}, 'destination': {'ip': '8.8.8.8'}, 'host': {'ip': ['192.168.5.1', '180.22.66.3']}, 'observer': {'ip': '10.10.2.33'}, 'server': {'ip': '10.10.2.33', 'nat': {'ip': '180.22.66.1'}}, 'source': {'ip': '10.10.2.33'}, 'related': {'ip': ['127.0.0.1', 'fe89::', '192.168.5.1', '8.8.8.8', '180.22.66.3', '10.10.2.33', '180.22.66.1', '223.2.3.2']}}

copies multiple fields to multiple target fields:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}}}
message: {'field': {'one': 1, 'two': 2, 'three': 3}}
processed: {'field': {'one': 1, 'two': 2, 'three': 3}, 'one': 1, 'two': 2, 'three': 3}

copies multiple fields to multiple target fields, while overwriting existing fields:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'overwrite_target': True}}
message: {'field': {'one': 1, 'two': 2, 'three': 3}, 'three': 'exists already'}
processed: {'field': {'one': 1, 'two': 2, 'three': 3}, 'one': 1, 'two': 2, 'three': 3}

copies multiple fields to multiple target fields, while one list will be extended:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'merge_with_target': True}}
message: {'field': {'one': 1, 'two': 2, 'three': 3}, 'three': ['exists already']}
processed: {'field': {'one': 1, 'two': 2, 'three': 3}, 'one': 1, 'two': 2, 'three': ['exists already', 3]}

copies multiple fields to multiple target fields, while one list will be extended with existing list:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'merge_with_target': True}}
message: {'field': {'one': 1, 'two': 2, 'three': [3, 3]}, 'three': ['exists already']}
processed: {'field': {'one': 1, 'two': 2, 'three': [3, 3]}, 'one': 1, 'two': 2, 'three': ['exists already', 3, 3]}

copies multiple fields to multiple target fields, while one target list will be overwritten with existing list:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'overwrite_target': True}}
message: {'field': {'one': 1, 'two': 2, 'three': [3, 3]}, 'three': ['exists already']}
processed: {'field': {'one': 1, 'two': 2, 'three': [3, 3]}, 'one': 1, 'two': 2, 'three': [3, 3]}

copies multiple fields to multiple target fields, while one source field is missing:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}}}
message: {'field': {'one': 1, 'three': 3}}
processed: {'field': {'one': 1, 'three': 3}, 'one': 1, 'three': 3, 'tags': ['_field_manager_missing_field_warning']}

moves multiple fields to multiple target fields:

rule: {'filter': 'field', 'field_manager': {'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'delete_source_fields': True}}
message: {'field': {'one': 1, 'two': 2, 'three': 3}}
processed: {'one': 1, 'two': 2, 'three': 3}

Combine fields to list and copy fields at the same time:

rule: {'filter': 'field', 'field_manager': {'source_fields': ['source.one', 'source.two'], 'target_field': 'merged', 'mapping': {'field.one': 'one', 'field.two': 'two', 'field.three': 'three'}, 'merge_with_target': True}}
message: {'field': {'one': 1, 'two': 2, 'three': 3}, 'source': {'one': ['a'], 'two': ['b']}}
processed: {'field': {'one': 1, 'two': 2, 'three': 3}, 'source': {'one': ['a'], 'two': ['b']}, 'one': 1, 'two': 2, 'three': 3, 'merged': ['a', 'b']}

Ignore missing fields: No warning and no failure tag if source field is missing:

rule: {'filter': 'field.a', 'field_manager': {'mapping': {'field.a': 'target_field', 'does.not.exists': 'target_field'}, 'ignore_missing_fields': True}}
message: {'field': {'a': 'first', 'b': 'second'}}
processed: {'field': {'a': 'first', 'b': 'second'}, 'target_field': 'first'}

merge_with_target preserves list ordering:

rule: {'filter': '(foo) OR (test)', 'field_manager': {'id': '5cfa7a26-94af-49de-bc82-460c42e9dc56', 'source_fields': ['foo', 'test'], 'target_field': 'existing_list', 'delete_source_fields': False, 'overwrite_target': False, 'merge_with_target': True}}
message: {'existing_list': ['hello', 'world'], 'foo': 'bar', 'test': 'value'}
processed: {'existing_list': ['hello', 'world', 'bar', 'value'], 'foo': 'bar', 'test': 'value'}

Convert existing target to list:

rule: {'filter': 'message', 'field_manager': {'source_fields': ['message'], 'target_field': 'new_field', 'merge_with_target': True}}
message: {'message': 'Value B', 'new_field': 'Value A'}
processed: {'message': 'Value B', 'new_field': ['Value A', 'Value B']}

Convert existing target to list with multiple source fields:

rule: {'filter': 'field1 OR field2 OR field3', 'field_manager': {'source_fields': ['field1', 'field2', 'field3'], 'target_field': 'new_field', 'merge_with_target': True}}
message: {'field1': 'Value B', 'field2': 'Value C', 'field3': 'Value D', 'new_field': 'Value A'}
processed: {'field1': 'Value B', 'field2': 'Value C', 'field3': 'Value D', 'new_field': ['Value A', 'Value B', 'Value C', 'Value D']}

Merge source dict into existing target dict:

rule: {'filter': 'source', 'field_manager': {'source_fields': ['source'], 'target_field': 'target', 'merge_with_target': True}}
message: {'source': {'source1': 'value'}, 'target': {'target1': 'value'}}
processed: {'source': {'source1': 'value'}, 'target': {'source1': 'value', 'target1': 'value'}}

Merge multiple source dicts into existing target dict:

rule: {'filter': 'source1', 'field_manager': {'source_fields': ['source1', 'source2', 'source3'], 'target_field': 'target', 'delete_source_fields': True, 'merge_with_target': True}}
message: {'source1': {'source1': 'value'}, 'source2': {'source2': 'value'}, 'source3': {'source-nested': {'foo': 'bar'}}, 'target': {'target1': 'value'}}
processed: {'target': {'source1': 'value', 'source2': 'value', 'source-nested': {'foo': 'bar'}, 'target1': 'value'}}

GenericAdder

The generic_adder is a processor that adds new fields and values to documents based on a list. The list resides inside a rule and/or inside a file.

Processor Configuration

- genericaddername:
    type: generic_adder
    rules:
        - tests/testdata/rules/rules

class logprep.processor.generic_adder.processor.GenericAdder.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The generic adder requires the additional field generic_adder. The field generic_adder.add can be defined. It contains a dictionary of field names and values that should be added. If dot notation is being used, then all fields on the path are being automatically created.

In the following example, the field some.added.field with the value some added value is being added.

Example with add

filter: add_generic_test
generic_adder:
  add:
    some.added.field: some added value
description: '...'

Alternatively, the additional field generic_adder.add_from_file can be added. It contains the path to a file with a YML file that contains a dictionary of field names and values that should be added to the document. Instead of a path, a list of paths can be used to add multiple files. All of those files must exist. If a list is used, it is possible to tell the generic adder to only use the first existing file by setting generic_adder.only_first_existing_file: true. In that case, only one file must exist.

In the following example a dictionary with field names and values is loaded from the file at PATH_TO_FILE_WITH_LIST. This dictionary is used like the one that can be defined via generic_adder.add.

Example with add_from_file

filter: 'add_generic_test'
generic_adder:
  add_from_file: PATH_TO_FILE_WITH_LIST
description: '...'

In the following example two files are being used.

Example with multiple files

filter: 'add_generic_test'
generic_adder:
  add_from_file:
    - PATH_TO_FILE_WITH_LIST
    - ANOTHER_PATH_TO_FILE_WITH_LIST
description: '...'

In the following example two files are being used, but only the first existing file is being loaded.

Example with multiple files and one loaded file

filter: 'add_generic_test'
generic_adder:
  only_first_existing_file: true
  add_from_file:
    - PATH_TO_FILE_THAT_DOES_NOT_EXIST
    - PATH_TO_FILE_WITH_LIST
description: '...'

class logprep.processor.generic_adder.rule.GenericAdderRule.Config

Config for GenericAdderRule

add: dict: Contains a dictionary of field names and values that should be added. If dot notation is being used, then all fields on the path are being automatically created.

add_from_file: list: Contains the path to a file with a YML file that contains a dictionary of field names and values that should be added to the document. Instead of a path, a list of paths can be used to add multiple files. All of those files must exist. For string format see Getters

only_first_existing_file: bool: If a list is used, it is possible to tell the generic adder to only use the first existing file by setting generic_adder.only_first_existing_file: true. In that case, only one file must exist.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

GenericResolver

The generic_resolver resolves log event values using regex lists.

Processor Configuration

- genericresolvername:
    type: generic_resolver
    rules:
        - tests/testdata/rules/rules

class logprep.processor.generic_resolver.processor.GenericResolver.Config

GenericResolver config

max_cache_entries: int | None: (Optional) Size of cache for results when resolving form a list. The cache can be disabled by setting it this option to 0.

cache_metrics_interval: int | None: (Optional) Cache metrics won’t be updated immediately. Instead updating is skipped for a number of events before it’s next update. cache_metrics_interval sets the number of events between updates (default: 1).

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The generic resolver requires the additional field generic_resolver. It works similarly to the hyperscan resolver, which utilizes hyperscan to process resolve lists. Configurable fields are being checked by regex patterns and a configurable value will be added if a pattern matches. The parameters within generic_resolver must be of the form field_mapping: {SOURCE_FIELD: DESTINATION_FIELD}, resolve_list: {REGEX_PATTERN_0: ADDED_VALUE_0, ..., REGEX_PATTERN_N: ADDED_VALUE_N}. SOURCE_FIELD will be checked by the regex patterns REGEX_PATTERN_[0-N] and a new field DESTINATION_FIELD with the value ADDED_VALUE_[0-N] will be added if there is a match. Adding the option "append_to_list": True makes the generic resolver write resolved values into a list so that multiple different values can be written into the same field.

In the following example to_resolve will be checked by the regex pattern .*Hello.*. "resolved": "Greeting" will be added to the event if the pattern matches the value in to_resolve.

Example

filter: to_resolve
generic_resolver:
  field_mapping:
    to_resolve: resolved
  resolve_list:
    .*Hello.*: Greeting

Alternatively, a YML file with a resolve list and a regex pattern can be used to resolve values. For this, a field resolve_from_file with the subfields path and pattern must be added. The resolve list in the file at path is then used in conjunction with the regex pattern in pattern. pattern must be a regex pattern with a capture group that is named mapping. The resolver will check for the pattern and get value captured by the mapping group. This captured value is then used in the list from the file.

ignore_case can be set to ignore the case when matching values that will be resolved. It is disabled by default. In the following example to_resolve: heLLo would be resolved, since ignore_case is set to true.

Example

filter: to_resolve
generic_resolver:
  field_mapping:
    to_resolve: resolved
  resolve_list:
    .*Hello.*: Greeting
  ignore_case: true

In the following example to_resolve will be checked by the regex pattern d*(?P<mapping>[a-z]+)d* and the list in path/to/resolve_mapping.yml will be used to add new fields. "resolved": "resolved foo" will be added to the event if the value in to_resolve begins with number, ends with numbers and contains foo. Furthermore, "resolved": "resolved bar" will be added to the event if the value in to_resolve begins with number, ends with numbers and contains bar.

Example resolving with list from file

filter: to_resolve
generic_resolver:
  field_mapping:
    to_resolve: resolved
  resolve_from_file:
    path: path/to/resolve_mapping.yml
    pattern: \d*(?P<mapping>[a-z]+)\d*

Example file with resolve list

foo: resolved foo
bar: resolved bar

class logprep.processor.generic_resolver.rule.GenericResolverRule.Config

RuleConfig for GenericResolver

field_mapping: dict: Mapping in form of {SOURCE_FIELD: DESTINATION_FIELD}

resolve_list: dict: lookup mapping in form of {REGEX_PATTERN_0: ADDED_VALUE_0, ..., REGEX_PATTERN_N: ADDED_VALUE_N}

resolve_from_file: dict: Mapping with a path key to a YML file (for string format see Getters) with a resolve list and a pattern key with a regex pattern which can be used to resolve values. The resolve list in the file at path is then used in conjunction with the regex pattern in pattern.

ignore_case: str | None: (Optional) Ignore case when matching resolve values. Defaults to False.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

GeoipEnricher

Processor to enrich log messages with geolocalization information

Processor Configuration

- geoipenrichername:
    type: geoip_enricher
    rules:
        - tests/testdata/geoip_enricher/rules
    db_path: /path/to/GeoLite2-City.mmdb

class logprep.processor.geoip_enricher.processor.GeoipEnricher.Config

geoip_enricher config

db_path: str: Path to a Geo2Lite city database by Maxmind in binary format. This must be provided separately. The file will be downloaded or copied and cached. For valid URI formats see Getters This product includes GeoLite2 data created by MaxMind, available from https://www.maxmind.com.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The geoip enricher requires the additional field geoip. The default output_field can be overridden using the optional parameter target_field. This can be a dotted subfield. The additional field geoip.source_fields must be given as list with one element. It contains the IP for which the geoip data should be added.

In the following example the IP in client.ip will be enriched with geoip data.

Example

filter: client.ip
geoip:
  source_fields: [client.ip]
description: '...'

class logprep.processor.geoip_enricher.rule.GeoipEnricherRule.Config

RuleConfig for GeoipEnricher

customize_target_subfields: dict

(Optional) Rewrites the default output subfield locations to custom output subfield locations. Must be in the form of key value mapping pairs (e.g. default_output: custom_output). Following default outputs can be customized:

type

geometry.type

geometry.coordinates

properties.accuracy_radius

properties.continent

properties.continent_code

properties.country

properties.country_iso_code

properties.time_zone

properties.city

properties.postal_code

properties.subdivision

A concrete example would look like this:

filter: client.ip
geoip:
  source_fields: [client.ip]
  customize_target_subfields:
    geometry.type: client.geo.type
    geometry.coordinates: client.geo.coordinates
description: '...'

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: Field to get geoip information for.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: Field for the output information. Defaults to geoip.

tests: List[Dict[str, str]]: Custom tests for this rule.

Grokker

The grokker processor dissects a message on a basis of grok patterns. This processor is based of the ideas of the logstash grok filter plugin. (see: https://www.elastic.co/guide/en/logstash/current/plugins-filters-grok.html)

The default builtin grok patterns shipped with logprep are the same than in logstash.

Processor Configuration

- my_grokker:
    type: grokker
    rules:
        - tests/testdata/rules/rules
    custom_patterns_dir: "http://the.patterns.us/patterns.zip"

class logprep.processor.grokker.processor.Grokker.Config

Config of Grokker

custom_patterns_dir: str: (Optional) A directory or URI to load patterns from. All files in all subdirectories will be loaded recursively. If an uri is given, the target file has to be a zip file with a directory structure in it.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given grokker rule

filter: message
grokker:
    mapping:
        message: "%{TIMESTAMP_ISO8601:@timestamp} %{LOGLEVEL:logLevel} %{GREEDYDATA:logMessage}"
description: 'an example log message'

Incoming event

{"message": "2020-07-16T19:20:30.45+01:00 DEBUG This is a sample log"}

Processed event

{
    "message": "2020-07-16T19:20:30.45+01:00 DEBUG This is a sample log",
    "@timestamp": "2020-07-16T19:20:30.45+01:00",
    "logLevel": "DEBUG",
    "logMessage": "This is a sample log"
}

class logprep.processor.grokker.rule.GrokkerRule.Config

Config for GrokkerRule

patterns: dict: (Optional) additional grok patterns as mapping. E.g. CUSTOM_PATTERN: [^s]* if you want to use special target fields, you are able to use them an usual in the mapping sections. Here you only have to declare the matching regex without named groups.

convert_datatype: dict: A mapping from source field and desired datatype [optional]. The datatypes could be float, int, bool, string

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A mapping from source fields to a grok pattern. Dotted field notation is possible in key and in the grok pattern. Additionally logstash field notation is possible in grok pattern. The value can be a list of search patterns or a single search pattern. Lists of search pattern will be checked in the order of the list until the first matching pattern. It is possible to use oniguruma regex pattern with or without grok patterns in the patterns part. When defining an oniguruma there is a limitation of three nested parentheses inside the pattern. Applying more nested parentheses is not possible. Logstashs ecs conform grok patterns are used to resolve the here used grok patterns. When writing patterns it is advised to be careful as the underlying regex can become complex fast. If the execution and the resolving of the pattern takes more than one second a matching timeout will be raised.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for grokker:

matches simple grok pattern:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'this is the %{USER:userfield}'}}}
message: {'message': 'this is the MyUser586'}
processed: {'message': 'this is the MyUser586', 'userfield': 'MyUser586'}

matches simple grok pattern with dotted field target:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'this is the %{USER:user.subfield}'}}}
message: {'message': 'this is the MyUser586'}
processed: {'message': 'this is the MyUser586', 'user': {'subfield': 'MyUser586'}}

matches simple grok pattern with logstash field target:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'this is the %{USER:[user][subfield]}'}}}
message: {'message': 'this is the MyUser586'}
processed: {'message': 'this is the MyUser586', 'user': {'subfield': 'MyUser586'}}

matches custom patterns:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'this is the %{CUSTOM_PATTERN:user.subfield}'}, 'patterns': {'CUSTOM_PATTERN': '[^\s]*'}}}
message: {'message': 'this is the MyUser586'}
processed: {'message': 'this is the MyUser586', 'user': {'subfield': 'MyUser586'}}

normalize from grok:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': '%{IP:some_ip} %{NUMBER:port:int}'}}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234'}}, 'some_ip': '123.123.123.123', 'port': 1234}

grok list match first matching after skipping non matching:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': ['%{IP:some_ip_1} %{NUMBER:port_1:int} foo', '%{IP:some_ip_2} %{NUMBER:port_2:int} bar']}}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}, 'some_ip_2': '123.123.123.123', 'port_2': 1234}

grok list match first matching after skipping non matching and does not match twice:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': ['%{IP:some_ip_1} %{NUMBER:port_1:int} foo', '%{IP:some_ip_2} %{NUMBER:port_2:int} bar', '%{IP:some_ip_3} %{NUMBER:port_3:int} bar']}}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}, 'some_ip_2': '123.123.123.123', 'port_2': 1234}

grok list match first matching after skipping non matching with same target fields:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': ['%{IP:some_ip} %{NUMBER:port:int} foo', '%{IP:some_ip} %{NUMBER:port:int} bar']}}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234 bar'}}, 'some_ip': '123.123.123.123', 'port': 1234}

normalization from nested grok:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': '%{IP:[parent][some_ip]} \w+ %{NUMBER:[parent][port]:int} %[ts]+ %{NUMBER:test:int}'}}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 555 1234 %ttss 11'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 555 1234 %ttss 11'}}, 'test': 11, 'parent': {'some_ip': '123.123.123.123', 'port': 1234}}

example log message:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': '%{TIMESTAMP_ISO8601:@timestamp} %{LOGLEVEL:logLevel} %{GREEDYDATA:logMessage}'}}}
message: {'message': '2020-07-16T19:20:30.45+01:00 DEBUG This is a sample log'}
processed: {'message': '2020-07-16T19:20:30.45+01:00 DEBUG This is a sample log', '@timestamp': '2020-07-16T19:20:30.45+01:00', 'logLevel': 'DEBUG', 'logMessage': 'This is a sample log'}

example for ecs conform output:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': '%{COMBINEDAPACHELOG}'}}}
message: {'message': '127.0.0.1 - - [11/Dec/2013:00:01:45 -0800] "GET /xampp/status.php HTTP/1.1" 200 3891 "http://cadenza/xampp/navi.php" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:25.0) Gecko/20100101 Firefox/25.0"'}
processed: {'message': '127.0.0.1 - - [11/Dec/2013:00:01:45 -0800] "GET /xampp/status.php HTTP/1.1" 200 3891 "http://cadenza/xampp/navi.php" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:25.0) Gecko/20100101 Firefox/25.0"', 'source': {'address': '127.0.0.1'}, 'timestamp': '11/Dec/2013:00:01:45 -0800', 'http': {'request': {'method': 'GET', 'referrer': 'http://cadenza/xampp/navi.php'}, 'version': '1.1', 'response': {'status_code': 200, 'body': {'bytes': 3891}}}, 'url': {'original': '/xampp/status.php'}, 'user_agent': {'original': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.9; rv:25.0) Gecko/20100101 Firefox/25.0'}}

matches simple oniguruma pattern:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'this is the (?<userfield>[A-Za-z0-9]+)'}}}
message: {'message': 'this is the MyUser586'}
processed: {'message': 'this is the MyUser586', 'userfield': 'MyUser586'}

oniguruma with nested parentheses (3 levels supported):

rule: {'filter': 'message', 'grokker': {'mapping': {'message': '^(?<timestamp>%{DAY}%{SPACE}%{MONTH}%{SPACE}%{MONTHDAY}%{SPACE}%{TIME}%{SPACE}%{YEAR})%{SPACE}%{GREEDYDATA:[remains]}$', 'remains': '(?<action>(SEND%{SPACE}INFO)%{SPACE}(?<info>BAL)%{GREEDYDATA:rest}'}}}
message: {'message': 'Wed Dec 7 13:14:13 2005 SEND INFO BAL/4'}
processed: {'message': 'Wed Dec 7 13:14:13 2005 SEND INFO BAL/4', 'timestamp': 'Wed Dec 7 13:14:13 2005', 'action': 'SEND INFO', 'info': 'BAL', 'rest': '/4', 'remains': 'SEND INFO BAL/4'}

two oniguruma with same target names, applies only the last target:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': '^(?<action>%{NUMBER})%{SPACE}(?<action>%{NUMBER})%{SPACE}(?<action>%{NUMBER})%{SPACE}(?<action>%{NUMBER})$'}}}
message: {'message': '13 37 21 42'}
processed: {'message': '13 37 21 42', 'action': '42'}

ignore_missing_fields:

rule: {'filter': 'winlog.event_id: 123456789', 'grokker': {'mapping': {'winlog.event_data.normalize me!': '%{IP:some_ip} %{NUMBER:port:int}', 'this_field_does_not_exist': '%{IP:some_ip} %{NUMBER:port:int}'}, 'ignore_missing_fields': True}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234'}}}
processed: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'normalize me!': '123.123.123.123 1234'}}, 'some_ip': '123.123.123.123', 'port': 1234}

Subfield with common prefix:

rule: {'filter': 'message', 'grokker': {'mapping': {'message': 'Facility %{USER:facility.location} %{USER:facility.location_level}'}}}
message: {'message': 'Facility spain primary'}
processed: {'message': 'Facility spain primary', 'facility': {'location': 'spain', 'location_level': 'primary'}}

HyperscanResolver

The hyperscan_resolver is a processor that can resolve fields by using a map of resolve patterns and resolve values. The map can be defined within rules or within a file. It uses python hyperscan to speedup the pattern matching. It works similarly to the generic resolver, but utilized hyperscan to process resolve lists.

For further information see: GenericResolver.

Processor Configuration

- hyperscanresolvername:
    type: hyperscan_resolver
    rules:
        - tests/testdata/rules/rules
    hyperscan_db_path: tmp/path/scan.db

class logprep.processor.hyperscan_resolver.processor.HyperscanResolver.Config

HyperscanResolver config

hyperscan_db_path: str: Path to a directory where the compiled Hyperscan databases will be stored persistently. Persistent storage is set to false per default. If the specified directory does not exist, it will be created. The database will be stored in the directory of the hyperscan_resolver if no path has been specified within the pipeline config. To update and recompile a persistently stored databases simply delete the whole directory. The databases will be compiled again during the next run.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The hyperscan resolver requires the additional field hyperscan_resolver.

The hyperscan resolver uses the Python Hyperscan library to check regex patterns. By default, the compiled Hyperscan databases will be stored persistently in the directory specified in the pipeline.yml. The field store_db_persistent can be used to configure if a database compiled from a rule’s resolve_list should be stored persistently.

class logprep.processor.hyperscan_resolver.rule.HyperscanResolverRule.Config

RuleConfig for HyperscanResolver

store_db_persistent: bool: Can be used to configure if a database compiled from a rule’s resolve_list should be stored persistently.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

field_mapping: dict: Mapping in form of {SOURCE_FIELD: DESTINATION_FIELD}

id: str: A uuid for the rule. Is generated by logprep.

ignore_case: str | None: (Optional) Ignore case when matching resolve values. Defaults to False.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

resolve_from_file: dict: A YML file with a resolve list and an optional regex pattern can be used to resolve values (for string format see Getters). For this, either a field resolve_from_file with a path to a resolve list file must be added or dictionary field resolve_from_file with the subfields path and pattern. Using the pattern option allows to define one regex pattern that can be used on all entries within a resolve list instead of having to write a regex pattern for each entry in the list.

resolve_list: dict: lookup mapping in form of {REGEX_PATTERN_0: ADDED_VALUE_0, ..., REGEX_PATTERN_N: ADDED_VALUE_N}

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

IpInformer

The ip_informer processor enriches an event with ip information.

Processor Configuration

- myipinformer:
    type: ip_informer
    rules:
        - tests/testdata/rules/rules

class logprep.processor.ip_informer.processor.IpInformer.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given ip_informer rule

filter: message
ip_informer:
    source_fields: ["ip"]
    target_field: result
description: '...'

Incoming event

{"ip": "192.168.5.1"}

Processed event

{
    "ip": "192.168.5.1",
    "result": {
        "192.168.5.1": {
            "compressed": "192.168.5.1",
            "exploded": "192.168.5.1",
            "is_global": false,
            "is_link_local": false,
            "is_loopback": false,
            "is_multicast": false,
            "is_private": true,
            "is_reserved": false,
            "is_unspecified": false,
            "max_prefixlen": 32,
            "reverse_pointer": "1.5.168.192.in-addr.arpa",
            "version": 4
        }
    }
}

class logprep.processor.ip_informer.rule.IpInformerRule.Config

Config for IPInformer

properties: list

(Optional) configures the properties to extract. Default is to extract all properties. Possible properties are: ['compressed', 'exploded', 'is_global', 'is_link_local', 'is_loopback', 'is_multicast', 'is_private', 'is_reserved', 'is_unspecified', 'max_prefixlen', 'reverse_pointer', 'version', 'compressed', 'exploded', 'ipv4_mapped', 'is_global', 'is_link_local', 'is_loopback', 'is_multicast', 'is_private', 'is_reserved', 'is_site_local', 'is_unspecified', 'max_prefixlen', 'reverse_pointer', 'scope_id', 'sixtofour', 'teredo', 'version'].

Default is to extract all available properties. If you explicitly want to extract a property, which does not exist for an IPAddress (e.g. toredo which is only given for IPv4Addresses), the property will be extracted with the value False.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for ip_informer:

single field with ipv4 address:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip'], 'target_field': 'result'}}
message: {'ip': '192.168.5.1'}
processed: {'ip': '192.168.5.1', 'result': {'192.168.5.1': {'compressed': '192.168.5.1', 'exploded': '192.168.5.1', 'is_global': False, 'is_link_local': False, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_unspecified': False, 'max_prefixlen': 32, 'reverse_pointer': '1.5.168.192.in-addr.arpa', 'version': 4}}}

single field with ipv6 address:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip'], 'target_field': 'result'}}
message: {'ip': 'fe80::2c71:58ff:fe6a:5a08'}
processed: {'ip': 'fe80::2c71:58ff:fe6a:5a08', 'result': {'fe80::2c71:58ff:fe6a:5a08': {'compressed': 'fe80::2c71:58ff:fe6a:5a08', 'exploded': 'fe80:0000:0000:0000:2c71:58ff:fe6a:5a08', 'ipv4_mapped': None, 'is_global': False, 'is_link_local': True, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_site_local': False, 'is_unspecified': False, 'max_prefixlen': 128, 'reverse_pointer': '8.0.a.5.a.6.e.f.f.f.8.5.1.7.c.2.0.0.0.0.0.0.0.0.0.0.0.0.0.8.e.f.ip6.arpa', 'scope_id': None, 'sixtofour': None, 'teredo': None, 'version': 6}}}

list field with ipv4 and ipv6 addresses:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip'], 'target_field': 'result'}}
message: {'ip': ['192.168.5.1', 'fe80::2c71:58ff:fe6a:5a08']}
processed: {'ip': ['192.168.5.1', 'fe80::2c71:58ff:fe6a:5a08'], 'result': {'192.168.5.1': {'compressed': '192.168.5.1', 'exploded': '192.168.5.1', 'is_global': False, 'is_link_local': False, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_unspecified': False, 'max_prefixlen': 32, 'reverse_pointer': '1.5.168.192.in-addr.arpa', 'version': 4}, 'fe80::2c71:58ff:fe6a:5a08': {'compressed': 'fe80::2c71:58ff:fe6a:5a08', 'exploded': 'fe80:0000:0000:0000:2c71:58ff:fe6a:5a08', 'ipv4_mapped': None, 'is_global': False, 'is_link_local': True, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_site_local': False, 'is_unspecified': False, 'max_prefixlen': 128, 'reverse_pointer': '8.0.a.5.a.6.e.f.f.f.8.5.1.7.c.2.0.0.0.0.0.0.0.0.0.0.0.0.0.8.e.f.ip6.arpa', 'scope_id': None, 'sixtofour': None, 'teredo': None, 'version': 6}}}

list and single field with ipv4 and ipv6 addresses:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip', 'single'], 'target_field': 'result'}}
message: {'ip': ['192.168.5.1', 'fe80::2c71:58ff:fe6a:5a08'], 'single': '127.0.0.1'}
processed: {'ip': ['192.168.5.1', 'fe80::2c71:58ff:fe6a:5a08'], 'single': '127.0.0.1', 'result': {'192.168.5.1': {'compressed': '192.168.5.1', 'exploded': '192.168.5.1', 'is_global': False, 'is_link_local': False, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_unspecified': False, 'max_prefixlen': 32, 'reverse_pointer': '1.5.168.192.in-addr.arpa', 'version': 4}, 'fe80::2c71:58ff:fe6a:5a08': {'compressed': 'fe80::2c71:58ff:fe6a:5a08', 'exploded': 'fe80:0000:0000:0000:2c71:58ff:fe6a:5a08', 'ipv4_mapped': None, 'is_global': False, 'is_link_local': True, 'is_loopback': False, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_site_local': False, 'is_unspecified': False, 'max_prefixlen': 128, 'reverse_pointer': '8.0.a.5.a.6.e.f.f.f.8.5.1.7.c.2.0.0.0.0.0.0.0.0.0.0.0.0.0.8.e.f.ip6.arpa', 'scope_id': None, 'sixtofour': None, 'teredo': None, 'version': 6}, '127.0.0.1': {'compressed': '127.0.0.1', 'exploded': '127.0.0.1', 'is_global': False, 'is_link_local': False, 'is_loopback': True, 'is_multicast': False, 'is_private': True, 'is_reserved': False, 'is_unspecified': False, 'max_prefixlen': 32, 'reverse_pointer': '1.0.0.127.in-addr.arpa', 'version': 4}}}

single field with ipv4 address and filtered properties:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip'], 'target_field': 'result', 'properties': ['is_loopback']}}
message: {'ip': '192.168.5.1'}
processed: {'ip': '192.168.5.1', 'result': {'192.168.5.1': {'is_loopback': False}}}

get field value for non existent property:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip'], 'target_field': 'result', 'properties': ['teredo']}}
message: {'ip': '192.168.5.1'}
processed: {'ip': '192.168.5.1', 'result': {'192.168.5.1': {'teredo': False}}}

ignore missing fields:

rule: {'filter': 'ip', 'ip_informer': {'source_fields': ['ip', 'does_not_exist'], 'target_field': 'result', 'properties': ['teredo'], 'ignore_missing_fields': True}}
message: {'ip': '192.168.5.1'}
processed: {'ip': '192.168.5.1', 'result': {'192.168.5.1': {'teredo': False}}}

KeyChecker

The key_checker processor checks if all field names in a provided list are given in the processed event.

Processor Configuration

- keycheckername:
    type: key_checker
    rules:
        - tests/testdata/rules/rules

class logprep.processor.key_checker.processor.KeyChecker.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The key_checker processor needs a list with at least one element in it. The Rule contains this list and it also contains a custom field where the processor can store all missing keys.

Given key_checker rule

filter: testkey
key_checker:
    source_fields:
        - key1
        - key2
    target_field: "missing_fields"
description: '...'

Incoming event

{
    "testkey": "key1_value",
    "_index": "value"
}

Processed event

{
    "testkey": "key1_value",
    "_index": "value",
    "missing_fields": "key1","key2"
}

class logprep.processor.key_checker.rule.KeyCheckerRule.Config

key_checker rule config

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: set: List of fields to check for.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to.

tests: List[Dict[str, str]]: Custom tests for this rule.

Labeler

Processor Configuration

- labelername:
    type: labeler
    schema: tests/testdata/labeler_rules/labeling/schema.json
    include_parent_labels: true
    rules:
        - tests/testdata/labeler_rules/rules

class logprep.processor.labeler.processor.Labeler.Config

Labeler Configurations

schema: str: Path to a labeling schema file. For string format see Getters.

include_parent_labels: bool | None: If the option is deactivated only labels defined in a rule will be activated. Otherwise, also allowed labels in the path to the root of the corresponding category of a label will be added. This allows to search for higher level labels if this option was activated in the rule.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The labeler requires the additional field label. The keys under label define the categories under which a label should be added. The values are a list of labels that should be added under a category.

In the following example, the label execute will be added to the labels of the category action:

Example

filter: 'command: "executing something"'
labeler:
    label:
        action:
        - execute
description: '...'

class logprep.processor.labeler.rule.LabelerRule.Config

RuleConfig for Labeler

label: dict: Mapping of a category and a list of labels to add

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

ListComparison

The list_comparison processor allows to compare values of source fields against lists provided as files.

Processor Configuration

- listcomparisonname:
    type: list_comparison
    rules:
        - tests/testdata/rules/rules
    list_search_base_path: /path/to/list/dir

class logprep.processor.list_comparison.processor.ListComparison.Config

ListComparison config

list_search_base_path: str: Relative list paths in rules will be relative to this path if this is set. This parameter is optional. For string format see Getters. You can also pass a template with keys from environment, e.g., ${<your environment variable>}. The special key ${LOGPREP_LIST} will be filled by this processor.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The list comparison enricher requires the additional field list_comparison. The mandatory keys under list_comparison are source_fields (as list with one element) and target_field. Former is used to identify the field which is to be checked against the provided lists. And the latter is used to define the parent field where the results should be written to. Both fields can be dotted subfields.

Additionally, a list or array of lists can be provided underneath the required field list_file_paths.

In the following example, the field user_agent will be checked against the provided list (priviliged_users.txt). Assuming that the value non_privileged_user will match the provided list, the result of the list comparison (in_list) will be added to the target field List_comparison.example.

Example Rule to compare a single field against a provided list.

filter: 'user_agent'
list_comparison:
    source_fields: ['user_agent']
    target_field: 'List_comparison.example'
    list_file_paths:
        - lists/privileged_users.txt
description: '...'

Note

Currently, it is not possible to check in more than one source_field per rule.

class logprep.processor.list_comparison.rule.ListComparisonRule.Config

RuleConfig for ListComparisonRule

list_file_paths: List[str]: List of files. For string format see Getters.

list_search_base_path: str: Base Path from where to find relative files from list_file_paths. You can also pass a template with keys from environment, e.g., ${<your environment variable>}. The special key ${LOGPREP_LIST} will be filled by this processor.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

PreDetector

The pre_detector is a processor that creates alerts for matching events. It adds MITRE ATT&CK data to the alerts.

Processor Configuration

- predetectorname:
    type: pre_detector
    rules:
        - tests/testdata/rules/rules
    outputs:
        - kafka: sre_topic
    alert_ip_list_path: /tmp/ip_list.yml

class logprep.processor.pre_detector.processor.PreDetector.Config

PreDetector config

outputs: tuple[dict[str, str]]: list of output mappings in form of output_name:topic. Only one mapping is allowed per list element

alert_ip_list_path: str

Path to a YML file or a list of paths to YML files with dictionaries of IPs. For string format see Getters. It is used by the PreDetector to throw alerts if one of the IPs is found in fields that were defined in a rule.

It uses IPs or networks in the CIDR format as keys and can contain expiration dates in the ISO format as values. If a value is empty, then there is no expiration date for the IP check. If a checked IP is covered by an IP and a network in the dictionary (i.e. IP 127.0.0.1 and network 127.0.0.0/24 when checking 127.0.0.1), then the expiration date of the IP is being used.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The predetector requires the additional field pre_detector.

The rule fields and a pre_detector_id are written into a custom output of the current output connector. The pre_detector_id will be furthermore added to the triggering event so that an event can be linked with its detection.

The following example shows a complete rule:

Example

filter: 'some_field: "very malicious!"'
pre_detector:
  case_condition: directly
  id: RULE_ONE_ID
  mitre:
  - attack.something1
  - attack.something2
  severity: critical
  title: Rule one
description: Some malicious event.

Applying this rule to the event

Example Input Event

{
  "some_field": "very malicious!",
}

would result in the following output and event enrichment

Enriched event

{
  "some_field": "very malicious!",
  "pre_detection_id": "80bfea3f-c24e-41d0-b82d-b2f02fc03ba9"
}

Generated extra output

{
  "@timestamp": "2023-06-16T08:23:41.000Z",
  "id": "RULE_ONE_ID",
  "title": "Rule one",
  "mitre": ["attack.something1", "attack.something2"],
  "case_condition": "directly",
  "rule_filter": "(some_field: 'very malicious!')",
  "severity": "critical",
  "pre_detection_id": "80bfea3f-c24e-41d0-b82d-b2f02fc03ba9",
  "description": "Some malicious event."
}

This generated extra output contains a corresponding rule_filter in lucene notation, which can be used to further investigate this rule in an existing OpenSearch.

Additionally, the optional field ip_fields can be specified. It allows to specify a list of fields that can be compared to a list of IPs, which can be configured in the pipeline for the predetector. If this field was specified, then the rule will only trigger in case one of the IPs from the list is also available in the specified fields.

Example

filter: 'some_field: something AND some_ip_field'
pre_detector:
  id: RULE_ONE_ID
  title: Rule one
  severity: critical
  mitre:
  - some_tag
  case_condition: directly
description: Some malicous event.
ip_fields:
- some_ip_field

The pre_detector also has the option to normalize the timestamp. To configure this the following parameters can be set in the rule configuration.

Example

filter: 'some_field: "very malicious!"'
pre_detector:
  case_condition: directly
  id: RULE_ONE_ID
  mitre:
  - attack.something1
  - attack.something2
  severity: critical
  title: Rule one
  timestamp_field: <field which includes the timestamp to be normalized>
  source_format: <the format of the timestamp in strftime format or ISO8601 or UNIX>
  sorce_timezone: <the timezone of the timestamp>
  target_timezone: <the timezone after normalization>
description: Some malicious event.

All of these new parameters are configurable and default to standard values if not explicitly set.

class logprep.processor.pre_detector.rule.PreDetectorRule.Config

RuleConfig for Predetector

title: str: A description for the triggered rule.

severity: str: Rating how dangerous an Event is, i.e. critical.

mitre: list: A list of MITRE ATT&CK tags.

case_condition: str: The type of the triggered rule, mostly directly.

ip_fields: list: Specify a list of fields that can be compared to a list of IPs, which can be configured in the pipeline for the pre_detector. If this field was specified, then the rule will only trigger in case one of the IPs from the list is also available in the specified fields.

sigma_fields: list | bool: tbd

link: str | None: A link to the rule if applicable.

source_format: list: the source format that can be given for normalizing the timestamp defaults to ISO8601

timestamp_field: str: the field which has the given timestamp to be normalized defaults to @timestamp

source_timezone: ZoneInfo: timezone of source_fields defaults to UTC

target_timezone: ZoneInfo: timezone for target_field defaults to UTC

failure_tags: list: tags to be added if processing of the rule fails

description: str: A description for the Rule. This has only documentation character.

id: str: An ID for the triggered rule.

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

tests: List[Dict[str, str]]: Custom tests for this rule.

Pseudonymizer

The pseudonymizer is a processor that pseudonymizes certain fields of log messages to ensure privacy regulations can be adhered to.

Security Best Practice - Processor - Pseudonymizer

The pseudonymizer works with two public keys for different roles. It is suggested to ensure that two different keys are being used such that the separation of the roles can be maintained.

It is suggested to use the GCM mode for encryption as it decouples the key length of the depseudo and analyst keys. This leads to additional 152 bytes of overhead for the encryption compared to the CTR mode encrypter.

Processor Configuration

- pseudonymizername:
    type: pseudonymizer
    rules:
        - tests/testdata/rules/rules
    outputs:
        - kafka: pseudonyms_topic
    pubkey_analyst: /path/to/analyst_pubkey.pem
    pubkey_depseudo: /path/to/depseudo_pubkey.pem
    hash_salt: secret_salt
    regex_mapping: /path/to/regex_mapping.json
    max_cached_pseudonyms: 1000000
    mode: GCM

class logprep.processor.pseudonymizer.processor.Pseudonymizer.Config

Pseudonymizer config

outputs: tuple[dict[str, str]]: list of output mappings in form of output_name:topic. Only one mapping is allowed per list element

pubkey_analyst: str

Path to the public key of an analyst. For string format see Getters.

/var/git/analyst_pub.pem

pubkey_depseudo: str

Path to the public key for depseudonymization. For string format see Getters.

/var/git/depseudo_pub.pem

hash_salt: str: A salt that is used for hashing.

regex_mapping: str

Path to a file (for string format see Getters) with a regex mapping for pseudonymization, i.e.:

/var/git/logprep-rules/pseudonymizer_rules/regex_mapping.json

max_cached_pseudonyms: int: The maximum number of cached pseudonyms. One cache entry requires ~250 Byte, thus 10 million elements would require about 2.3 GB RAM. The cache is not persisted. Restarting Logprep does therefore clear the cache. This caching reduces the CPU load of Logprep (no demanding encryption must be performed repeatedly) and the load on subsequent components (i.e. Logstash or Opensearch). In case the cache size has been exceeded, the least recently used entry is deleted. Has to be greater than 0.

max_cached_pseudonymized_urls: int: The maximum number of cached pseudonymized urls. Default is 10000. Behaves similarly to the max_cached_pseudonyms. Has to be greater than 0.

mode: str: Optional mode of operation for the encryption. Can be either ‘GCM’ or ‘CTR’. Default is ‘GCM’.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The pseudonymizer requires the additional field pseudonymizer.mapping. It contains key value pairs that define what will be pseudonymized.

They key represents the field that will be pseudonymized and the value contains a regex keyword. The regex keyword defines which parts of the value are being pseudonymized. Only the regex matches are being pseudonymized that are also in a capture group. An arbitrary amount of capture groups can be used. The definitions of regex keywords are located in a separate file.

In the following the field event_data.param1 is being completely pseudonymized. This is achieved by using the predefined keyword RE_WHOLE_FIELD, which will be resolved to a regex expression. RE_WHOLE_FIELD resolves to (.*) which puts the whole match in a capture group and therefore pseudonymizes it completely.

Example - Rule

filter: 'event_id: 1 AND source_name: "Test"'
pseudonymizer:
    mapping:
        event_data.param1: RE_WHOLE_FIELD
description: '...'

Example - Regex mapping file

{
  "RE_WHOLE_FIELD": "(.*)",
  "RE_DOMAIN_BACKSLASH_USERNAME": "\w+\\(.*)",
  "RE_IP4_COLON_PORT": "([\d.]+):\d+"
}

class logprep.processor.pseudonymizer.rule.PseudonymizerRule.Config

RuleConfig for Pseudonymizer

url_fields: list: url fields to pseudonymize

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: mapping of field to regex string

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Requester

A processor to invoke http requests. Can be used to enrich events from an external api or to trigger external systems by and with event field values.

Security Best Practice - Processor - Requester

As the requester can execute arbitrary http requests it is advised to execute requests only against known and trusted endpoints and that the communication is protected with a valid SSL-Certificate. Do so by setting a certificate path with the option cert. To ensure that the communication is trusted it is also recommended to set either an Authorization-Header or a corresponding authentication with a username and password, via auth.

Processor Configuration

- requestername:
    type: requester
    rules:
        - tests/testdata/rules/rules

class logprep.processor.requester.processor.Requester.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The Requester is configured by the keyword requester. It can be used to trigger external systems via web request or enrich eventdata by external apis.

A speaking example for event enrichment via external api:

Given requester rule

filter: 'domain'
requester:
  url: https://internal.cmdb.local/api/v1/locations
  method: POST
  target_field: cmdb.location
  headers:
    Authorization: Bearer askdfjpiowejf283u9r
  json:
    hostname: ${message.hostname}
description: '...'

Incoming event

{"message": {"hostname": "BB37293hhj"}}

Raw response json data given from the api

{
    "city": "Montreal",
    "Building": "L76",
    "Floor": 3,
    "Room": 34
}

Processed event

{"message": {"hostname": "BB37293hhj"},
 "cmdb": {
     "location": {
         "city": "Montreal",
         "Building": "L76",
         "Floor": 3,
         "Room": 34
         }
    }
}

class logprep.processor.requester.rule.RequesterRule.Config

Config for RequesterRule

target_field_mapping: dict: (Optional) A mapping from dotted_fields to dotted_fields to extract data from response json to target fields. If target_field is given too, this is made additionally

method: str: The method for the request. must be one of GET, OPTIONS, HEAD, POST, PUT, PATCH, DELETE

url: str: The url for the request. You can use dissect pattern language to add field values

json: dict: (Optional) The json payload. Can be enriched with event data by using the pattern ${the.dotted.field} to retrieve nested field values.

data: str: (Optional) The data payload. Can be enriched with event data by using the pattern ${the.dotted.field} to retrieve nested field values.

params: dict: (Optional) The query parameters as dictionary. Can be enriched with event data by using the pattern ${the.dotted.field} to retrieve nested field values.

headers: dict: (Optional) The http headers as dictionary.

auth: tuple: (Optional) The authentication tuple. Defined as list. Will be converted to tuple

timeout: float: (Optional) The timeout in seconds as float for the request. Defaults to 2 seconds

verify: bool: (Optional) Whether or not verify the ssl context. Defaults to True.

proxies: dict: (Optional) Dictionary mapping protocol or protocol and host to the URL of the proxy (e.g. {"http": "foo.bar:3128", "http://host.name": "foo.bar:4012"}) to be used on the request

cert: str: (Optional) SSL client certificate as path to ssl client cert file (.pem).

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: (Optional) The target field to write the complete response json or body to

tests: List[Dict[str, str]]: Custom tests for this rule.

SelectiveExtractor

The selective_extractor is a processor that allows to write field values of a given log message to a different Kafka topic. The output topic is configured via the pipeline yml, while the fields to be extracted are specified by means of a list which is also specified in the pipeline configuration as a file path. This processor is applied to all messages, because of that it does not need further rules to specify it’s behavior.

Processor Configuration

- selectiveextractorname:
    type: selective_extractor
    rules:
        - tests/testdata/rules/rules

class logprep.processor.selective_extractor.processor.SelectiveExtractor.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The selective extractor requires the additional field selective_extractor. It contains a list of field names that should be extracted (source_fields) and list of output mappings to which they should be send to (outputs). If dotted notation is being used, then all fields on the path are being automatically created.

In the following example, the field field.extract with the value extracted value is being extracted and send to the output named kafka and the topic named topic_to_send_to.

Example rule with extract from field list

filter: extract_test
selective_extractor:
  source_fields: ["field.extract", "field2", "field3"]
  outputs:
    - kafka: topic_to_send_to
description: '...'

Example event

{
  "extract_test": {
    "field": {
      "extract": "extracted value"
    }
  }
}

Extracted event from Example

{
  "extract": "extracted value"
}

Alternatively, the additional field selective_extractor.extract.extract_from_file can be added. It contains the path to a text file with a list of fields per line to be extracted.

Example rule with extract from file

filter: extract_test
selective_extractor:
    extract_from_file: /path/to/file
    outputs:
        - opensearch: topic_to_send_to
description: '...'

Example of file with field list

field1
field2
field3

The file has to exist.

It is possible to mix both extraction sources. They will be merged to one list without duplicates.

Example rule with extract from file

filter: extract_test
selective_extractor:
    extract_from_file: /path/to/file
    source_fields: ["field1", "field2", "field4"]
    outputs:
      - kafka: topic_to_send_to
description: '...'

Example of file with field list

field1
field2
field3

class logprep.processor.selective_extractor.rule.SelectiveExtractorRule.Config

RuleConfig for SelectiveExtractor

outputs: tuple[dict[str, str]]: list of output mappings in form of output_name:topic. Only one mapping is allowed per list element

extract_from_file: str: The path or url to a file with a flat list of fields to extract. For string format see Getters.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to True

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: List of fields in dotted field notation

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

StringSplitter

The string_splitter processor splits string by whitespace (default) or a given delimiter and writes the resulting list to a target field.

Processor Configuration

- samplename:
    type: string_splitter
    rules:
        - tests/testdata/rules/rules

class logprep.processor.string_splitter.processor.StringSplitter.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given string_splitter rule

filter: message
string_splitter:
    source_fields: ["message"]
    target_field: result
description: '...'

Incoming event

{"message": "this is the message"}

Processed event

{"message": "this is the message", "result": ["this", "is", "the", "message"]}

class logprep.processor.string_splitter.rule.StringSplitterRule.Config

Config for StringSplitterRule

delimiter: str: The delimiter for splitting. Defaults to whitespace

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for string_splitter:

splits without delimeter on whitespace:

rule: {'filter': 'message', 'string_splitter': {'source_fields': ['message'], 'target_field': 'result'}}
message: {'message': 'this is the message'}
processed: {'message': 'this is the message', 'result': ['this', 'is', 'the', 'message']}

splits with delimeter:

rule: {'filter': 'message', 'string_splitter': {'source_fields': ['message'], 'target_field': 'result', 'delimiter': ', '}}
message: {'message': 'this, is, the, message'}
processed: {'message': 'this, is, the, message', 'result': ['this', 'is', 'the', 'message']}

TemplateReplacer

The template_replacer is a processor that can replace parts of a text field to anonymize those parts. The replacement is based on a template file.

Processor Configuration

- templatereplacername:
    type: template_replacer
    rules:
        - tests/testdata/rules/rules
    template: /tmp/template.yml
    pattern:
        delimiter: ","
        fields:
            - field.name.a
            - field.name.b
        allowed_delimiter_field: field.name.b
        target_field: target.field

class logprep.processor.template_replacer.processor.TemplateReplacer.Config

TemplateReplacer config

template: str: Path to a YML file (for path format see Getters) with a list of replacements in the format %{provider_name}-%{event_id}: %{new_message}.

pattern: dict

Configures how to use the template file by specifying the following subfields:

delimiter - Delimiter to use to split the template
fields - A list of dotted fields that are being checked by the template.
allowed_delimiter_field - One of the fields in the fields list can contain the delimiter. This must be specified here.
target_field - The field that gets replaced by the template.

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The template replacer requires the additional field template_replacer. No additional configuration parameters are required for the rules. The module is completely configured over the pipeline configuration.

In the following example the target field specified in the processor configuration is replaced for all log messages that have winlog.provider_name and winlog.event_id if it is defined in the template file.

Example

filter: winlog.provider_name AND winlog.event_id
template_replacer: {}
description: ''

class logprep.processor.template_replacer.rule.TemplateReplacerRule.Config

Config for FieldManagerRule

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

tests: List[Dict[str, str]]: Custom tests for this rule.

Timestamper

The timestamper processor normalizes timestamps to iso8601 compliant output format.

Processor Configuration

- myteimestamper:
    type: timestamper
    rules:
        - tests/testdata/rules/rules

class logprep.processor.timestamper.processor.Timestamper.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

A speaking example:

Given timestamper rule

filter: "winlog.event_id: 123456789"
timestamper:
    source_fields: ["winlog.event_data.some_timestamp_utc"]
    target_field: "@timestamp"
    source_format: UNIX
    source_timezone: UTC
    target_timezone: Europe/Berlin
description: example timestamper rule

Incoming event

    {
        "winlog": {
            "api": "wineventlog",
            "event_id": 123456789,
            "event_data": {"some_timestamp_utc": "1642160449"},
        }
    }

Processed event

    {
        "@timestamp": "2022-01-14T12:40:49+01:00",
        "winlog": {
            "api": "wineventlog",
            "event_id": 123456789,
            "event_data": {"some_timestamp_utc": "1642160449"},
        },
    }

class logprep.processor.timestamper.rule.TimestamperRule.Config

Config for TimestamperRule

source_format: list

A list of possible source formats if source_fields is not an iso8601 compliant time format string the format must be given in the syntax of the python builtin datetime.strptime (see: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes). Additionally, the value ISO8601 (default) and UNIX can be used in the list of the source_formats field. The former can be used if the timestamp already exists in the ISO8601 format, such that only a timezone conversion should be applied. And the latter can be used if the timestamp is given in the UNIX Epoch Time. This supports the Unix timestamps in seconds and milliseconds. Be aware that UNIX and ISO8601 formats do not validate the completeness of input string. If you want to ensure, the completeness of the input string, you have to use the datetime.strptime syntax.

For example, the following time formats are valid ISO8601 formats:

hh:mm
hh:mm:ss
hh:mm:ss.sss
hhmmss.ssssss
hhmm
hhmmss

The output string will always be in this format: 2000-12-31T22:59:59Z. As you can see the output string has a time with seconds. If the input string does not have a time or the time does not have seconds, the output string will have seconds or times set to zero.

If you don’t want this behavior, you have to use the datetime.strptime syntax. With this syntax, the timestamper`errors out with a :code:`TimeParserException and a tag _timestamper_failure will be added to the event.

source_timezone: ZoneInfo: timezone of source_fields. defaults to UTC

target_timezone: ZoneInfo: timezone for target_field. defaults to UTC

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The field from where to get the time from as list with one element

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to, defaults to @timestamp

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for timestamper:

parses iso8601 without pattern:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'target_field': '@timestamp'}}
message: {'message': '2009-06-15 13:45:30Z'}
processed: {'message': '2009-06-15 13:45:30Z', '@timestamp': '2009-06-15T13:45:30Z'}

parses iso8601 to default target field:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message']}}
message: {'message': '2009-06-15 13:45:30Z'}
processed: {'message': '2009-06-15 13:45:30Z', '@timestamp': '2009-06-15T13:45:30Z'}

parses by datetime source format:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'source_format': '%Y %m %d - %H:%M:%S'}}
message: {'message': '2000 12 31 - 22:59:59'}
processed: {'message': '2000 12 31 - 22:59:59', '@timestamp': '2000-12-31T22:59:59Z'}

converts timezone information:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'source_format': '%Y %m %d - %H:%M:%S', 'source_timezone': 'UTC', 'target_timezone': 'Europe/Berlin'}}
message: {'message': '2000 12 31 - 22:59:59'}
processed: {'message': '2000 12 31 - 22:59:59', '@timestamp': '2000-12-31T23:59:59+01:00'}

parses unix timestamp:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'source_format': 'UNIX', 'source_timezone': 'UTC', 'target_timezone': 'Europe/Berlin'}}
message: {'message': '1642160449843'}
processed: {'message': '1642160449843', '@timestamp': '2022-01-14T12:40:49.843000+01:00'}

normalization from timestamp berlin to utc:

rule: {'filter': 'winlog.event_id: 123456789', 'timestamper': {'source_fields': ['winlog.event_data.some_timestamp_berlin'], 'target_field': '@timestamp', 'source_format': '%Y %m %d - %H:%M:%S', 'source_timezone': 'Europe/Berlin', 'target_timezone': 'UTC'}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_berlin': '1999 12 12 - 12:12:22'}}}
processed: {'@timestamp': '1999-12-12T11:12:22Z', 'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_berlin': '1999 12 12 - 12:12:22'}}}

normalization from timestamp same timezone:

rule: {'filter': 'winlog.event_id: 123456789', 'timestamper': {'source_fields': ['winlog.event_data.some_timestamp_utc'], 'target_field': '@timestamp', 'source_format': '%Y %m %d - %H:%M:%S', 'source_timezone': 'UTC', 'target_timezone': 'UTC'}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1999 12 12 - 12:12:22'}}}
processed: {'@timestamp': '1999-12-12T12:12:22Z', 'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1999 12 12 - 12:12:22'}}}

normalization from unix with millis timestamp:

rule: {'filter': 'winlog.event_id: 123456789', 'timestamper': {'source_fields': ['winlog.event_data.some_timestamp_utc'], 'target_field': '@timestamp', 'source_format': 'UNIX', 'source_timezone': 'UTC', 'target_timezone': 'Europe/Berlin'}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1642160449843'}}}
processed: {'@timestamp': '2022-01-14T12:40:49.843000+01:00', 'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1642160449843'}}}

normalization from unix with seconds timestamp:

rule: {'filter': 'winlog.event_id: 123456789', 'timestamper': {'source_fields': ['winlog.event_data.some_timestamp_utc'], 'target_field': '@timestamp', 'source_format': 'UNIX', 'source_timezone': 'UTC', 'target_timezone': 'Europe/Berlin'}}
message: {'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1642160449'}}}
processed: {'@timestamp': '2022-01-14T12:40:49+01:00', 'winlog': {'api': 'wineventlog', 'event_id': 123456789, 'event_data': {'some_timestamp_utc': '1642160449'}}}

attempt parsing with multiple patterns, second one successful:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'source_format': ['%Y %m %d', '%Y %m %d - %H:%M:%S']}}
message: {'message': '2000 12 31 - 22:59:59'}
processed: {'message': '2000 12 31 - 22:59:59', '@timestamp': '2000-12-31T22:59:59Z'}

attempt parsing with multiple patterns, both successful but stopping after first:

rule: {'filter': 'message', 'timestamper': {'source_fields': ['message'], 'source_format': ['%Y %m %d - %H:%M:%S', '%Y %m %d - %H:%M:%S']}}
message: {'message': '2000 12 31 - 22:59:59'}
processed: {'message': '2000 12 31 - 22:59:59', '@timestamp': '2000-12-31T22:59:59Z'}

TimestampDiffer

The timestamp_differ can calculate the time difference between two timestamps.

Processor Configuration

- timestampdiffer_name:
    type: timestamp_differ
    rules:
        - tests/testdata/rules/rules

class logprep.processor.timestamp_differ.processor.TimestampDiffer.Config

Common Configurations

rules: List[str]: List of rule locations to load rules from. In addition to paths to file directories it is possible to retrieve rules from a URI. For valid URI formats see Getters. As last option it is possible to define entire rules with all their configuration parameters as list elements.

tree_config: str | None: Path to a JSON file with a valid Rule Tree Configuration. For string format see Getters.

apply_multiple_times: bool | None: Set if the processor should be applied multiple times. This enables further processing of an output with the same processor.

type: str: Type of the component

health_timeout: float

Default is 1 seconds

Type:: Timeout in seconds for health check

Rule Configuration

The timestamp format can be specified per timestamp.

A speaking example:

Given timestamp differ rule

filter: 'ingest AND processed'
timestamp_differ:
  diff: ${processed:%Y-%m-%d %H:%M:%S} - ${ingest:%Y-%m-%d %H:%M:%S}
  target_field: processing_time
  output_format: seconds
description: '...'

Incoming event

{"ingest": "2022-12-06 10:00:00", "processed": "2022-12-06 10:00:05"}

Processed event

{"ingest": "2022-12-06 10:00:00", "processed": "2022-12-06 10:00:05", "processing_time": "5.0"}

class logprep.processor.timestamp_differ.rule.TimestampDifferRule.Config

Config for TimestampDiffer

diff: str: Specifies the timestamp subtraction and their respective timestamp formats. The fields and the timestamp format can be specified in the form of: ${dotted.field.path:timestamp-format}. If no timestamp format is given, e.g. ${dotted.field.path}, the string will be assumed as an iso8601 compliant string and parsed. For more information on the format syntax see datetime strftime/strptime.

source_field_formats: list

output_format: str: (Optional) Specifies the desired output format of the timestamp difference, allowed values are: seconds, milliseconds, nanoseconds, defaults to: seconds.

show_unit: bool: (Optional) Specifies whether the unit (s, ms, ns) should be part of the output. Defaults to False.

delete_source_fields: bool: Whether to delete all the source fields or not. Defaults to False

description: str: A description for the Rule. This has only documentation character.

id: str: A uuid for the rule. Is generated by logprep.

ignore_missing_fields: bool: If set to True missing fields will be ignored, no warning is logged and the event is not tagged with the failure tag. Defaults to False

mapping: dict: A key-value mapping from source fields to target fields. Can be used to copy/move multiple fields at once. If you want to move fields set delete_source_fields to true. Works independent of source_fields and target_field.

merge_with_target: bool: If the target field exists and is a list, the list will be extended with the values of the source fields. If the source field is a list, the lists will be merged by appending the source fields list to the target list. If the source field is a dict, the dict will be merged with the target dict. If the source keys exist in the target dict, the values will be overwritten. So this is not e deep merge. If the target field does not exist, a new field will be added with the source field value as list or dict. Defaults to False.

overwrite_target: bool: Overwrite the target field value if exists. Defaults to False

regex_fields: list: It is possible to use regex expressions to match values. For this, the field name with the regex pattern in the rule filter must be added to the optional field regex_fields in the rule definition.

source_fields: list: The fields from where to get the values which should be processed, requires target_field.

tag_on_failure: list: A list of tags which will be appended to the event on non critical errors, defaults to ["_<rule_type>_failure"]. Is currently only used by the Dissector and FieldManager.

target_field: str: The field where to write the processed values to. Can be used to move/copy single values, merge multiple values to one list or extend a list. Requires source_field.

tests: List[Dict[str, str]]: Custom tests for this rule.

Examples for timestamp_differ:

Time difference between two timestamps:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2} - ${field1}', 'target_field': 'time_diff'}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278.0'}

Time difference between two timestamps with day change:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2} - ${field1}', 'target_field': 'time_diff'}}
message: {'field1': '2022-12-04 12:00:00', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-04 12:00:00', 'field2': '2022-12-05 12:00:00', 'time_diff': '86400.0'}

Time difference between two timestamps with timezone information:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S %z} - ${field1:%Y-%m-%d}', 'target_field': 'time_diff'}}
message: {'field2': '2022-05-09 03:56:47 -03:00', 'field1': '2022-05-08'}
processed: {'field2': '2022-05-09 03:56:47 -03:00', 'field1': '2022-05-08', 'time_diff': '111407.0'}

Time difference between two timestamps with full weekday and month:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%A, %d. %B %Y %I:%M%p} - ${field1:%Y-%m-%d}', 'target_field': 'time_diff'}}
message: {'field2': 'Monday, 05. December 2022 11:19AM', 'field1': '2022-12-05'}
processed: {'field2': 'Monday, 05. December 2022 11:19AM', 'field1': '2022-12-05', 'time_diff': '40740.0'}

Time difference between two timestamps with AM/PM :

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%a %b %d %I:%M:%S %p %Y} - ${field1:%Y-%m-%d}', 'target_field': 'time_diff'}}
message: {'field2': 'Wed Dec 4 1:14:31 PM 2022', 'field1': '2022-12-03'}
processed: {'field2': 'Wed Dec 4 1:14:31 PM 2022', 'field1': '2022-12-03', 'time_diff': '134071.0'}

Time difference between two timestamps with milliseconds output:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'output_format': 'milliseconds'}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278000.0'}

Time difference between two timestamps with nanoseconds output:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'output_format': 'nanoseconds'}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278000000000.0'}

Time difference between two timestamps in subfield:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff'}}
message: {'field1': '2022-12-05 11:38:42', 'subfield': {'field2': '2022-12-05 12:00:00'}}
processed: {'field1': '2022-12-05 11:38:42', 'subfield': {'field2': '2022-12-05 12:00:00'}, 'time_diff': '1278.0'}

Time difference between two timestamps without specific timestamp format:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff'}}
message: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}}
processed: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}, 'time_diff': '5922.0'}

Time difference between two timestamps with removal of source fields:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'delete_source_fields': True}}
message: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}}
processed: {'time_diff': '5922.0'}

Time difference between two timestamps with overwriting of target:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'overwrite_target': True}}
message: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}, 'time_diff': 'some content'}
processed: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}, 'time_diff': '5922.0'}

Time difference between two timestamps with extension of existing list in target field:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'merge_with_target': True}}
message: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}, 'time_diff': ['some content']}
processed: {'field1': '2022-12-05 12:00:00', 'subfield': {'field2': '2022-12-05T11:38:42-02:00'}, 'time_diff': ['some content', '5922.0']}

Timestamp diff with integer field (unix epoch):

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1}', 'target_field': 'time_diff'}}
message: {'field1': 1670234400, 'subfield': {'field2': '2022-12-05 12:00:00'}}
processed: {'field1': 1670234400, 'subfield': {'field2': '2022-12-05 12:00:00'}, 'time_diff': '7200.0'}

Timestamp diff with difference in milliseconds, output in seconds:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1}', 'target_field': 'time_diff'}}
message: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}}
processed: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}, 'time_diff': '0.3'}

Timestamp diff with difference in milliseconds, output in milliseconds:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1}', 'target_field': 'time_diff', 'output_format': 'milliseconds'}}
message: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}}
processed: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}, 'time_diff': '300.0'}

Timestamp diff with difference in milliseconds, output in nanoseconds:

rule: {'filter': 'field1 AND subfield.field2', 'timestamp_differ': {'diff': '${subfield.field2} - ${field1}', 'target_field': 'time_diff', 'output_format': 'nanoseconds'}}
message: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}}
processed: {'field1': '2022-12-05 12:00:00.200', 'subfield': {'field2': '2022-12-05 12:00:00.500'}, 'time_diff': '300000000.0'}

Time difference between two timestamps with negative result:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2} - ${field1}', 'target_field': 'time_diff'}}
message: {'field2': '2022-12-09', 'field1': '2022-12-10'}
processed: {'field2': '2022-12-09', 'field1': '2022-12-10', 'time_diff': '-86400.0'}

Time difference between two timestamps with visible second unit:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'output_format': 'seconds', 'show_unit': True}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278.0 s'}

Time difference between two timestamps with visible millisecond unit:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'output_format': 'milliseconds', 'show_unit': True}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278000.0 ms'}

Time difference between two timestamps with visible nanosecond unit:

rule: {'filter': 'field1 AND field2', 'timestamp_differ': {'diff': '${field2:%Y-%m-%d %H:%M:%S} - ${field1:%Y-%m-%d %H:%M:%S}', 'target_field': 'time_diff', 'output_format': 'nanoseconds', 'show_unit': True}}
message: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00'}
processed: {'field1': '2022-12-05 11:38:42', 'field2': '2022-12-05 12:00:00', 'time_diff': '1278000000000.0 ns'}