DMSALA: A Dynamic Multi-Subpopulation Artificial Lemming Algorithm for Feature Selection in IoT Intrusion Detection

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With the rapid growth of the Internet of Things, intrusion detection systems face severe challenges in processing massive, high-dimensional, and redundant network traffic while satisfying strict low-latency and high-efficiency requirements. To address these challenges,this paper improves the original artificial lemming algorithm (ALA) and proposes a dynamic multi-subpopulation artificial lemming algorithm (DMSALA) for feature selection, and then constructs an intrusion detection framework for IoT based on DMSALA. The proposed DMSALA introduces an adaptive clustering-based dynamic multi-subpopulation structure to alleviate premature convergence during the search process. In addition, a cosine-based nonlinear weighting strategy is designed to achieve a smooth transition toward the global optimum during optimization, while an adaptive Gaussian perturbation mechanism is incorporated to enhance local exploitation capability. To verify the effectiveness of the proposed DMSALA, its optimization performance is first evaluated on the CEC2017 benchmark functions and compared with several other classical algorithms. The experimental results show that DMSALA exhibits better convergence accuracy and stability on the benchmark problems. Furthermore, the NF-ToN-IoT-v2 and NF-BoT-IoT-v2 dataset are employed to conduct feature selection experiments, where classification performance, feature reduction capability and computational cost are comprehensively analyzed. In both multiclass and binary classification tasks, the proposed DMSALA strikes a better trade-off between feature reduction and classification accuracy. The results indicate that DMSALA can provide a reasonable balance between detection effectiveness and computational overhead, making it more suitable for low-latency and efficient IoT intrusion detection scenarios.

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