Adaptive Threat Mitigation in PoW Blockchains (Part II): A Deep Reinforcement Learning Approach to Countering Evasive...

Static defense mechanisms in blockchain security, while effective against known threats, are inherently vulnerable to intelligent adversaries who can adapt their strategies to evade detection. This paper addresses this critical limitation by proposing a next-generation adaptive security framework powered by deep reinforcement learning (DRL). Building upon the state-of-the-art statistical detection system presented in Part I of this series, we introduce a DRL agent that learns to dynamically adjust security parameters in response to evolving network conditions and adversarial behavior. The agent is trained using a realistic, proxy-based reward function that optimizes for network stability without requiring ground-truth attack labels. We conduct comprehensive evaluation across multiple scenarios, demonstrating that our DRL-enhanced framework consistently renders attacks unprofitable where static models eventually fail. Against adaptive adversaries, the DRL agent drives adversary profit to -42±13% (deeply unprofitable) compared to +65±22% (profitable) under the static framework and +145±18% under baseline detectors. Furthermore, we demonstrate resilience in zero-day scenarios where novel attack variants are suppressed within 24 h, and compare performance against alternative AI methodologies (supervised learning, GANs), achieving a superior F1-score of 0.95±0.02. This work provides a robust blueprint for creating intelligent, adaptive, and resilient security systems for future decentralized networks.