REINFORCEMENT LEARNING WITH TRANSPARENT POLICIES AN EXPLAINABLE AI APPROACH TO ADAPTIVE CYBER SECURITY

Abstract

There is an increasing need for cybersecurity systems that are intuitive and adaptable, capable of making real-time decisions in response to increasingly sophisticated cyber threats. Reinforcement learning (RL) systems can identify the optimal course of action by analyzing their interactions with their surroundings. Due to this, it may be an effective approach for recognizing and mitigating the development of threats. Traditional reinforcement learning models are not always the optimal choice for critical security scenarios due to their opaque nature, or "black boxes." Consequently, the objective of this research is to examine the application of explicit principles in RL systems to the use of explainable AI (XAI) in adaptive cybersecurity. The proposed method not only guarantees the prompt detection of threats but also promotes the adherence to security regulations and human surveillance through the use of policy models that are easily comprehensible and provide explicit reasoning for each decision. Experimental evidence suggests that by employing transparent reinforcement learning rules, we can more effectively understand system behavior and readily adjust to emergent cyber threats. The research elucidates how the integration of reinforcement learning and explainable AI can enhance the reliability, accountability, and trustworthiness of next-generation cybersecurity systems