Transforming Cybersecurity Dynamics: Enhanced Self-Play Reinforcement Learning in Intrusion Detection and Prevention System

In the rapidly evolving realm of cybersecurity, the need for dynamic and adaptive defense mechanisms is paramount. This article introduces an innovative approach to intrusion detection and prevention systems (IDPS) through the application of self-play reinforcement learning. We extend the existing framework by integrating a model-free, off-policy algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3), to enhance the automated response capabilities of IDPS. This advancement results in a more effective and adaptable system capable of responding to the dynamically changing landscape of cyber threats. Furthermore, we introduce an innovative policy strategy within the TD3 framework, coupled with substantial auto-regression enhancements. These enhancements significantly improve the robustness and adaptability of cybersecurity response infrastructures, equipping them to better handle evolving cyber threats. Our methodology involves modelling intrusion prevention as a zero-sum game using Markov games, which captures the dynamic interaction between a defender and an attacker. The paper showcases the effectiveness of these approaches through enhanced self-play, policy refinement, and simulation scenarios, indicating significant improvements in threat detection and response over traditional security mechanisms. The findings underscore the potential of enhanced autoregressive policy representation to reshape the landscape of intrusion prevention strategies, making it a promising candidate for broader applications in cybersecurity.