This repository is no longer the main one, it has been forked and is now included in the SigmaHQ organisation.
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This is the SQLite backend for pySigma. It provides the package sigma.backends.sqlite
with the sqliteBackend
class.
This backend also aims to be compatible with Zircolite which uses pure SQLite queries to perform SIGMA-based detection on EVTX, Auditd, Sysmon for linux, XML or JSONL/NDJSON Logs.
It supports the following output formats:
- default: plain SQLite queries
- zircolite : SQLite queries in JSON format for Zircolite
This backend is currently maintained by:
- Full text search support will need some work and is not a priority since it needs virtual tables on SQLite side
- In a future update, changing table name will be handled by a backend option
- Aggregation is not supported since it is deprecated by the sigma specification and there are nearly no rule using it in the official repository
poetry add pysigma-pipeline-sysmon
poetry add pysigma-pipeline-windows
from sigma.collection import SigmaCollection
from sigma.backends.sqlite import sqlite
from sigma.pipelines.sysmon import sysmon_pipeline
from sigma.pipelines.windows import windows_logsource_pipeline
from sigma.processing.resolver import ProcessingPipelineResolver
# Create the pipeline resolver
piperesolver = ProcessingPipelineResolver()
# Add pipelines
piperesolver.add_pipeline_class(sysmon_pipeline()) # Syssmon
piperesolver.add_pipeline_class(windows_logsource_pipeline()) # Windows
# Create a combined pipeline
combined_pipeline = piperesolver.resolve(piperesolver.pipelines)
# Instantiate backend using the combined pipeline
sqlite_backend = sqlite.sqliteBackend(combined_pipeline)
rule = SigmaCollection.from_yaml(
r"""
title: Test
status: test
logsource:
category: test_category
product: test_product
detection:
sel:
fieldA: valueA
fieldB: valueB
condition: sel
""")
print(sqlite_backend.convert(rule)[0])
poetry run python3 example.py