Reinforcement Learning Approaches Integrated with Hybrid Models for Proactive Threat Prevention
D. Shobana · 2025
The growing complexity and sophistication of insider threats have prompted the development of advanced detection systems that can proactively identify malicious activities from within an organization. This book chapter explores the integration of reinforcement learning (RL) with hybrid models for insider threat detection, focusing on the effectiveness of these approaches in real-time monitoring, threat assessment, and risk mitigation. By leveraging RL's adaptive capabilities, combined with other techniques such as anomaly detection, Natural Language Processing (NLP), and behavioral analysis, these hybrid models offer a comprehensive solution to combat insider threats. Key challenges, including data privacy concerns, ethical implications, and the design of effective reward functions, are examined to ensure the responsible and efficient application of these models. The chapter further emphasizes the importance of continuous learning mechanisms, dynamic risk assessments, and the incorporation of penalties and rewards based on the severity of threats. Through this hybrid framework, organizations can achieve a balance between safeguarding critical assets and maintaining privacy standards. This work presents a roadmap for the implementation of intelligent, adaptive, and ethical insider threat detection systems, paving the way for future research in cybersecurity applications.