评分 5.5 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-17
评分依据：End-to-end LLM agent for network incident response, replaces RL simulators with log-based learning, practical security ops application
arXiv:2602.13156v2 Announce Type: replace-cross Abstract: Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively explored the reinforcement learning approach, which involves learning response strategies through extensive simulation of the incident. While this approach can be effective, it requires handcrafted modeling of the simulator and suppresses useful semantics from raw system logs and alerts.