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A collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.
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README.md

 Cyber Log Accelerators (CLX)

NOTE: For the latest stable README.md ensure you are on the master branch.

CLX ("clicks") provides a collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.

The goal of CLX is to:

  1. Provide SIEM integration with GPU compute environments via RAPIDS and effectively extend the SIEM environment,
  2. Make available pre-built use cases that demonstrate CLX and RAPIDS functionality that are ready to use in a Security Operations Center (SOC),
  3. Allow cyber data scientists and SecOps teams to generate workflows, using cyber-specific GPU-accelerated primitives and methods, that let them interact with code using security language, and
  4. Accelerate log parsing in a flexible, non-regex method.

Getting Started

CLX is targeted towards cybersecurity data scientists, senior security analysts, threat hunters, and forensic investigators. Data scientists can use CLX in traditional Python files and Jupyter notebooks. CLX also includes structure in the form of a workflow. A workflow is a series of data transformations performed on a GPU dataframe that contains raw cyber data, with the goal of surfacing meaningful cyber analytical output. Multiple I/O methods are available, including Kafka and on-disk file stores.

Example flow workflow reading and writing to file:

from clx.workflow import netflow_workflow

source = {
   "type": "fs",
   "input_format": "csv",
   "input_path": "/path/to/input",
   "schema": ["firstname","lastname","gender"],
   "delimiter": ",",
   "required_cols": ["firstname","lastname","gender"],
   "dtype": ["str","str","str"],
   "header": "0"
}
dest = {
   "type": "fs",
   "output_format": "csv",
   "output_path": "/path/to/output"
}
wf = netflow_workflow.NetflowWorkflow(source=source, destination=dest, name="my-netflow-workflow")
wf.run_workflow()

Example Notebooks

The notebooks folder contains example use cases and workflow instantiations.

Installation

CLX is available in a Docker container, by building from source, and through Conda installation.

CLX Docker Container

Pull the RAPIDS container and build for CLX.

docker pull rapidsai/rapidsai-dev-nightly:0.11-cuda10.0-devel-ubuntu18.04-py3.7
docker build -t clx .
docker run --runtime=nvidia \
  --rm -it \
  -p 8888:8888 \
  -p 8787:8787 \
  -p 8686:8686 \
  clx:latest

Start a new CLX container. The container also includes Kafka.

docker-compose up

Install from Source

# Run tests
pip install pytest
pytest

# Build and install
python setup.py install

Conda install

conda install -c rapidsai-nightly -c rapidsai -c nvidia -c pytorch -c conda-forge -c defaults clx

Contributing

For contributing guildelines please reference our guide for contributing.

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