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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 2·5 min read
A Hybrid Insider Threat Detection Framework Combining Multi-Agent Simulation, Layered SIEM Correlation, and Theory-of-Mind Reasoning
TL;DR
A hybrid insider threat detection framework was developed, combining multi-agent simulation and advanced reasoning techniques, achieving significant improvements in detection accuracy and reduced false positives. The best variant, EG-SIEM-Enron, reached an F1 score of 0.944, demonstrating its effectiveness in identifying malicious insiders.
✦ Why It Matters
Consider implementing evidence-gated SIEM strategies to enhance insider threat detection in your organization.
Key Takeaways
- Explore multi-agent simulation for threat detection improvements.
- Implement evidence gating to reduce false positives effectively.
- Fine-tune classifiers for better performance across different email domains.
- Evaluate detection frameworks against traditional anomaly detection methods.
How It Works
The framework employs multi-agent simulation to model insider behavior and integrates layered SIEM correlation to analyze various data streams, including email communications. Theory-of-Mind reasoning allows the system to infer intentions behind actions, enhancing the detection of malicious insiders.
Evidence gating filters out irrelevant data, focusing on high-confidence indicators of insider threats.