A Cyber Range Evaluation of Autonomous Network Incident Response Agents

The study evaluates autonomous agents that respond to network intrusions within a cyber range designed for human operator training. Using an emulated network with variable topology, red‑team attacks, and simulated users, the agents aim to block unauthorized access while minimizing defensive costs. Experiments compare heuristic policies with reinforcement‑learning‑derived policies, finding that the latter generally defend more efficiently, though performance varies with adversary strategy and user simulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents
• reinforcement-learning