MAFSIDS ： A reinforcement learning-based intrusion detection model for multi-intelligence feature selection networks

Abstract In the era of big data, the growing number of cyber assaults poses a significant danger to network services. Intrusion detection systems (IDS) rely on the quality of its features to accurately identify cyber threats. Nowadays prevalent IDS prefer to create intricate neural networks and pay less attention to the problem of feature selection. In this study, we present a multi-intelligence feature selection network intrusion detection model based on reinforcement learning. The model extracts feature information of network traffic by means of a graph convolutional neural network (GCN), using multiple deep Q-network (DQN)-based intelligences to decide whether the corresponding features are selected, and then trains classifiers to identify network attacks by means of deep reinforcement learning (DRL). We examined the model's performance using both the NSL-KDD and CSE-CIC-IDS2018 datasets. The simulation experimental results demonstrate that MAFSIDS is able to extract accurate feature information from the input data via the GCN network, and that the multi-intelligence will then select the optimal feature subset and learn the data via DRL to ultimately enhance the model's cyber attack recognition performance. In the era of big data, the model has vast application potential and provides a solid assurance for network security.