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AI Research
arXiv CS.AI·Jun 9·5 min read
Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR
TL;DR
Commercial endpoint detection and response (EDR) systems have evolved from manual configurations to autonomous AI-driven components, creating a gap in evaluation methods. An evaluation framework for autonomous defense agents using EDR was developed to assess their effectiveness in real-world scenarios.
✦ Why It Matters
Engineers can leverage this framework to enhance the evaluation and deployment of autonomous cyber defense solutions.
Key Takeaways
- Implement tailored evaluation methodologies for autonomous defense agents.
- Ensure EDR telemetry aligns with scientific benchmarking needs.
- Differentiate between agent and EDR actions during assessments.
- Monitor EDR behavior variability throughout evaluation periods.
How It Works
The evaluation framework utilizes a controlled environment (GOAD lab) to simulate real-world scenarios, allowing researchers to benchmark autonomous defense agents against established EDR systems. By employing large language models, the framework assesses the agents' decision-making capabilities and their interaction with the EDR.