Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-agent reinforcement learning (MARL) can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it as ACD$^3$-GAT (Adaptive Constrained Counterfactual Decisioning with a Graph Attention Network encoder), an architecture that separates simulator observations from reusable operational budgets, constrained optimization, graph state encoding, and counterfactual action screening. We evaluate the method in CAGE Challenge 4, where agents operate under budgets for Mean Time to Recover (MTTR), false...
| Authors | Jose Luis Lima de Jesus Silva |
|---|---|
| Primary category | cs.MA |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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