Explainable Reinforcement Learning for Adaptive Cyber Defense in Encrypted Networks

The growing adoption of encrypted traffic in enterprise and critical-infrastructure networks has created a defensive paradox: while encryption protects data in transit, it also limits the visibility defenders traditionally rely on to detect and respond to threats. This paper explores an explainable reinforcement learning (XRL) framework designed to support adaptive cyber defense in such visibility-constrained environments. The proposed approach treats the network as a dynamic decision space in which an RL agent learns to anticipate suspicious behavior, reconfigure defenses, and allocate monitoring resources without decrypting traffic. To maintain operational trust, we integrate explainability mechanisms that translate the agent’s actions into interpretable cues—highlighting the observed patterns, state transitions, and reward dynamics that influenced each decision. Through this combination of adaptability and transparency, the framework aims to support more resilient intrusion response, reduce false positives, and offer network analysts a clearer understanding of machine-driven defense strategies. The insights presented here outline how XRL can help modernize cyber defense practices for encrypted networks, where traditional signature-driven and static rule-based methods increasingly fall short.