AI-Driven Cybersecurity: A Reinforcement LearningBased Approach for Adaptive Intrusion Detection Systems
Elias Andersson · International Journal of AI BigData Computational and Management Studies · 2024
In the rapidly evolving landscape of cybersecurity, traditional intrusion detection systems (IDS) often fall short in adapting to novel and sophisticated threats. This paper explores the application of reinforcement learning (RL) to develop adaptive intrusion detection systems (AIDS) that can dynamically learn and improve their detection capabilities. We present a comprehensive framework that integrates RL with IDS to create a system capable of continuous learning and adaptation. The proposed approach leverages the strengths of RL in handling complex and uncertain environments, enabling the IDS to evolve and enhance its performance over time. We evaluate the proposed system using a variety of datasets and metrics, demonstrating its effectiveness in detecting and responding to both known and unknown threats. The results indicate that the RL-based AIDS outperforms traditional IDS in terms of accuracy, adaptability, and response time