DGWO: A Deep Reinforcement Learning-Driven Grey Wolf Optimizer for Feature Selection in Network Intrusion Detection Systems
Qianqian Zhang, Ting Shu, Jinsong Xia
With the continuous evolution of network attack techniques, efficiently selecting the most discriminative feature subset from massive network traffic data has become a key issue for improving the performance of intrusion detection systems. Metaheuristic algorithms, as a core approach for wrapper-based feature selection, directly determine the quality of the selected feature subset through their optimization capability. The Grey Wolf Optimizer (GWO) is popular due to its simple structure and few parameters, where three leader wolves guide the search through weighted cooperation. However, its static weight mechanism cannot adapt to dynamic changes in individual search states and population evolution stages, limiting optimization capability and convergence performance. To address this issue, this study proposes a Deep Reinforcement Learning-based Grey Wolf Optimizer (DGWO), which pre-trains a weight adjustment decision model offline and dynamically adjusts the guiding weights of leader wolves during the online search process, thereby improving the optimization ability of the algorithm. Experimental results on NSL-KDD, UNSW-NB15, and CIC-IDS-2017 datasets show that DGWO outperforms seven comparative feature selection methods. It achieves classification accuracies of 93.59%, 93.40%, and 94.84%, respectively, demonstrating superior performance in accuracy, precision, recall, and F1-score. DGWO promotes symmetry between cybersecurity requirements and reliable intrusion detection.