Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning
Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat (APT) actors exploit "Living off the Land" techniques and targeted telemetry perturbations to induce ambiguity in monitoring systems, causing automated defenses to overreact or misclassify benign behavior as malicious activity. Existing monolithic and multi-agent defense pipelines largely operate on correlation-based signals, lack structural constraints on response actions, and are vulnerable to reasoning drift under ambiguous or adversarial inputs. We present the Causal Multi-Agent D...
| Authors | Yiyao Zhang, Diksha Goel, Hussain Ahmad |
|---|---|
| Primary category | cs.CR |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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