Deep Reinforcement Learning based Intrusion Detection System with Feature Selections Method and Optimal Hyper-parameter in IoT Environment

Abstract

The continuous rise of inter-contented Internet of Things (IoT) devices has significantly increased network traffic, complexity, and the ever-changing Internet environment, making them more vulnerable to security attacks. Therefore, a robust and elegant intrusion detection system (IDS) based on advanced machine learning methods is required for securing the IoT environment. This paper discusses the new deep reinforcement learning (DRL) based network intrusion detection system (NIDS) with feature selection methods. However, the structure and training of the DRL model are still challenging tasks. Moreover, the effectiveness and accuracy of DRL-IDS crucially depend on the suitable hyper-parameters adaptation, i.e., differing hyperparameters can result in markedly varied IDS performance. Furthermore, due to the commercial value of hyper-parameters, confidentiality may be deemed necessary, and proprietary algorithms may protect their exclusive use. Therefore, we find different optimal hyper-parameters values for the training of DRL agents. Furthermore, we evaluate the effectiveness of different hyper-parameters both theoretically and empirically. For instance, we assess the hyper-parameters for the case of varying routing systems and countermeasures and integrate the optimal hyper-parameters for various network performances.