Intrusion Detection System Using Deep Reinforcement Learning

Abstract

Intrusion Detection Systems (IDS) play a critical role in ensuring the security and integrity of computer networks by identifying and mitigating potential threats. Feature selection is a fundamental task in IDS, aiming to select a subset of relevant features from the vast amount of available data to improve detection accuracy and reduce computational overhead. Traditional feature selection methods often rely on heuristics or statistical techniques, which may not fully exploit the complex relationships among features in high-dimensional data. In this research paper, we propose a novel approach to feature selection using deep reinforcement learning (DRL) techniques. The objective of our research is to develop an intelligent IDS feature selection model that can automatically learn the most discriminative features for accurate intrusion detection. Our proposed approach combines the strengths of deep learning and reinforcement learning, enabling the IDS to autonomously explore and exploit the feature space for optimal decision-making. We employ a deep neural network as the feature extractor, which extracts high-level representations from raw input data, and integrate it with a reinforcement learning agent that selects features based on rewards obtained from an environment simulator. To evaluate the effectiveness of our approach, we conduct experiments using benchmark datasets commonly used in intrusion detection research. We compare the performance of our DRL-based feature selection approach with traditional feature selection techniques, such as information gain, chi-square, and genetic algorithms. We measure the effectiveness of feature selection in terms of detection accuracy, computational efficiency, and robustness against adversarial attacks. This research paper contributes to the field of intrusion detection by introducing a novel approach to feature selection using deep reinforcement learning. The results highlight the potential of DRL techniques in enhancing the performance and efficiency of IDSs, paving the way for further advancements in intrusion detection systems and network security.