Reinforcement Learning for Automated Intrusion Detection and Adaptive Defense in Zero-Day Attack Scenarios
Krishna Kumar, L K Aishwaryaa, K K Pradeep · 2025
The increasing sophistication of cyber threats, particularly zero-day attacks, necessitates the development of intelligent and adaptive security mechanisms capable of real-time threat detection and mitigation. Traditional intrusion detection and prevention systems (IDPS) rely on static rule sets and signature-based techniques, which are insufficient against novel and evolving attack vectors. Reinforcement Learning (RL) offers a promising approach by enabling autonomous agents to learn optimal defense strategies through continuous interaction with network environments. This chapter explores the application of RL for automated intrusion detection and adaptive defense, focusing on its ability to enhance cyber resilience against zero-day attacks. It provides a comprehensive overview of RL-based threat detection frameworks, highlighting key methodologies such as Deep Q-Networks (DQN), actor-critic models, and deep reinforcement learning (DRL) architectures. the chapter examines the challenges associated with RL deployment in cybersecurity, including adversarial manipulation, computational complexity, and data scarcity, it discusses the integration of RL with security information and event management (SIEM) systems, real-time anomaly detection, and self-learning security policies. The proposed RL-driven approach enhances proactive threat hunting capabilities, minimizes false positives, and enables adaptive response mechanisms to emerging cyber threats. By leveraging RL techniques, cybersecurity frameworks can transition from reactive models to autonomous, self-evolving defense systems, ensuring enhanced protection in dynamic and adversarial environments.