Multi-strategy RIME optimization algorithm for feature selection of network intrusion detection

Abstract

Feature selection in network intrusion detection is an important research hotspot in network security. The performance of meta-heuristic algorithms, as one of the most effective methods for feature selection, will directly affect the solution to the problem. The RIME optimization algorithm, a novel meta-heuristic algorithm proposed in 2023 based on the physical phenomenon of rime, is suitable for intrusion detection feature selection due to its simplicity and efficiency. However, the standard RIME algorithm suffers from low convergence accuracy and a tendency to converge early, which severely limits its problem-solving ability. For this reason, this paper proposes an improved feature selection algorithm, the Multi-strategy RIME optimization algorithm (MRIME), which combines the chaotic local search strategy, an interaction mechanism, and an improved hard-rime puncture mechanism to enhance the performance of the standard RIME algorithm. The proposed MRIME algorithm has been validated through experiments on three publicly available intrusion detection datasets: UNSW-NB15, CIC-IDS-2017, and CICIoV2024. The experimental results demonstrate that MRIME outperforms existing feature selection algorithms, excelling in accuracy, precision, recall, F1 and runtime. Furthermore, MRIME has proven its adaptability to high-dimensional, low-dimensional, and large-scale datasets through scalability experiments on nine UCI datasets. These findings highlight the potential of MRIME for feature selection in intrusion detection systems.