End-to-End Integration of Reinforcement Learning and Deep Q-Networks for Autonomous Cyber Threat Remediation
Ayush Kumar, S Mohan · 2025
This book chapter explores the end-to-end integration of Reinforcement Learning (RL) and Deep Q-Networks (DQN) for autonomous cyber threat remediation. It highlights the evolution of cybersecurity challenges and the potential of artificial intelligence to address these issues in real-time environments. By combining RL's decision-making capabilities with DQN's deep learning architecture, the chapter introduces a novel framework for detecting, analyzing, and mitigating cyber threats autonomously. The discussion covers the design, development, and deployment of RL-DQN systems, focusing on the scalability, generalization, and performance evaluation of such models in dynamic cybersecurity landscapes. Key challenges, including adversarial attacks and integration with existing security infrastructures, are critically analyzed. Ethical, regulatory, and societal implications of adopting AI-driven cybersecurity solutions are examined. This comprehensive analysis provides a strategic perspective on the future of autonomous security systems.