Processing Pipelines

This documentation page describes the concepts and classes of pySigma that can be used for transformation of Sigma rules.

Sigma rules are tranformed to take care of differences between the Sigma rule and the target data model. Examples are differences in field naming schemes or value representation.

Resolvers

Pipeline resolvers resolve identifiers and file names into a consolidated processing pipeline and take care of the appropriate ordering via the priority property that should be contained in a processing pipeline.

A processing pipeline resolver is a sigma.processing.resolver.ProcessingPipelineResolver object. It is initialized with an mapping between identifiers and sigma.processing.pipeline.ProcessingPipeline objects or callables that return such objects.

The method sigma.processing.resolver.ProcessingPipelineResolver.resolve_pipeline() returns a ProcessingPipeline object corresponsing with the given identifier or contained in the specified YAML file. sigma.processing.resolver.ProcessingPipelineResolver.resolve() returns a consolidated pipeline with the appropriate ordering as specified by the priority property of the specified pipelines.

Processing Pipeline

Classes

Specifying Processing Pipelines as YAML

A processing pipeline can be specified as YAML file that can be loaded with ProcessingPipeline.from_yaml(yaml) or by specifying a filename to ProcessingPipelineResolver.resolve() or ProcessingPipelineResolver.resolve_pipeline().

The following items are expected on the root level of the YAML file:

  • name: the name of the pipeline.

  • priority: specifies the ordering of the pipeline in case multiple pipelines are concatenated. Lower priorities are used first.

  • transformations: contains a list of transformation items.

Some conventions used for processing pipeline priorities are:

Priority

Description

10

Log source pipelines like for Sysmon.

20

Pipelines provided by backend packages that should be run before the backend pipeline.

50

Backend pipelines that are integrated in the backend and applied automatically.

60

Backend output format pipelines that are integrated in the backend and applied automatically for the asscoiated output format.

Pipelines with the same priority are applied in the order they were provided. Pipelines without a priority are assumed to have the priority 0.

Transformation items are defined as a map as follows:

  • id: the identifier of the item. This is also tracked at detection item or condition level and can be used in future conditions.

  • type: the type of the transformation as specified in the identifier to class mappings below: Transformations

  • Arbitrary transformation parameters are specified at the samle level.

  • rule_conditions, detection_item_conditions, field_name_conditions: conditions of the type corresponding to the name.

Conditions are specified as follows:

  • type: defines the condition type. It must be one of the identifiers that are defined in Conditions

  • Arbitrary conditions parameters are specified on the same level.

Example:

name: Custom Sysmon field naming
priority: 100
transformations:
- id: field_mapping
    type: field_name_mapping
    mapping:
        CommandLine: command_line
    rule_conditions:
    - type: logsource
        service: sysmon

Conditions

New in version 0.8.0: Field name conditions.

There are three types of conditions:

  • Rule conditions are evaluated to the whole rule. They are defined in the rule_conditions attribute of a ProcessingItem.

  • Detection item conditions are evaluated for each detection item. They are defined in the detection_item_conditions attribute of a ProcessingPipeline.

  • Field name conditions are evaluated for field names that can be located in detection items or in the field name list of a Sigma rule. They are defined in the field_name_conditions attribute of a ProcessingPipeline.

In addition to the *_conditions attributes of ProcessingPipeline objects, there are two furthert attributes hat control the condition matching behavior:

  • rule_condition_linking, detection_item_condition_linking and field_name_condition_linking: one of any or all functions. Controls if one or all of the conditions from the list must match to result in an overall match.

  • rule_condition_negation, detection_item_condition_negation and field_name_condition_negation: if set to True, the condition result is negated.

The results of the evaluatuon of different condition types are and-linked. E.g. if a processing item contains rule and field name conditions, both must evaluate to True to get the overall result of True.

Rule Conditions

Detection Item Identifiers

Identifier

Class

logosurce

LogsourceCondition

contains_detection_item

RuleContainsDetectionItemCondition

processing_item_applied

RuleProcessingItemAppliedCondition

Detection Item Conditions

Detection Item Identifiers

Identifier

Class

match_string

MatchStringCondition

processing_item_applied

DetectionItemProcessingItemAppliedCondition

Field Name Conditions

Field Name Identifiers

Identifier

Class

include_fields

IncludeFieldCondition

exclude_fields

ExcludeFieldCondition

processing_item_applied

FieldNameProcessingItemAppliedCondition

Base Classes

Base classes must be overridden to implement new conditions that can be used in processing pipelines. In addition, the new class should be mapped to an identifier. This allows to use the condition from processing pipelines defined in YAML files. The mapping is done in the dict rule_conditions or detection_item_conditions in the sigma.processing.conditions package for the respective condition types. This is not necessary for conditions that should be uses privately and not be distributed via the main pySigma distribution.

Transformations

Implemented Transformations

The following transformations with their corresponding identifiers for usage in YAML-based pipeline definitions are available:

Detection Item Identifiers

Identifier

Class

field_name_mapping

FieldMappingTransformation

field_name_suffix

AddFieldnameSuffixTransformation

field_name_prefix

AddFieldnamePrefixTransformation

drop_detection_item

DropDetectionItemTransformation

wildcard_placeholders

WildcardPlaceholderTransformation

value_placeholders

ValueListPlaceholderTransformation

query_expression_placeholders

QueryExpressionPlaceholderTransformation

add_condition

AddConditionTransformation

change_logsource

ChangeLogsourceTransformation

replace_string

ReplaceStringTransformation

rule_failure

RuleFailureTransformation

detection_item_failure

DetectionItemFailureTransformation

Base Classes

There are four transformation base classes that can be derived to implement transformations on particular parts of a Sigma rule or the whole Sigma rule:

Transformation Tracking

tbd