Enhancing Intrusion Detection Systems with Reinforcement Learning: A Comprehensive Survey of RL-based Approaches and Techniques

Intrusion detection systems (IDSs) play a crucial role in network security, as the need for secure networks continues to grow. However, traditional IDSs are not able to accurately and efficiently detect attacks due to the vast amount of data generated in a network and the emergence of new types of attacks every day. To address this challenge, researchers have recently turned to reinforcement learning (RL) techniques to enhance IDSs’ accuracy and efficiency. RL is a powerful machine learning approach that can learn from experience and adapt to changing environments, making it a promising choice for IDS applications. In this paper, we present a comprehensive survey of RL-based IDSs. We provide an overview of various RL algorithms applied to IDSs and discuss the different types of features used to represent network traffic data. Additionally, we highlight the challenges and future directions in the field to improve the performance of RL-based IDSs.