Dynamic Adaptive Intrusion Detection System Using Hybrid Reinforcement Learning

The research aims to develop a novel Intrusion Detection System (IDS) using computational intelligence, specifically focusing on a hybrid reinforcement learning approach. Unlike traditional IDS that rely on static rule-based approaches, this IDS will dynamically adapt and learn from network traffic patterns, allowing it to detect and respond to emerging threats effectively. By developing an IDS based on hybrid reinforcement learning that can dynamically adapt to emerging threats, this research can significantly contribute to the field of cybersecurity and aid in building more robust and proactive defense mechanisms for network security. The concepts of Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Monte Carlo Tree Search (MCTS) are reinforcement learning algorithms that can be applied in a Dynamic Adaptive Intrusion Detection System (IDS) to enhance its adaptability and decision-making capabilities. Each algorithm contributes unique strengths to different aspects of the IDS’s dynamic adaptation process.