Feature selection method for network intrusion based on hybrid meta-heuristic dynamic optimization algorithm

As network attacks become increasingly frequent, ensuring the effectiveness of network intrusion detection systems remains critical to network security. Hybrid metaheuristic-based feature selection methods suffer from poor initial population quality, slow convergence speed, and a tendency to fall into local optimality when processing high-dimensional data. These issues reduce the efficiency and accuracy of network intrusion detection. To address these challenges, a hybrid metaheuristic feature selection method, HMDOA, is proposed. This method enhances detection efficiency and accuracy by optimizing the feature selection process. In the population initialization stage, an enhanced population generation mechanism is introduced to increase the diversity of initial solutions in the feature space distribution and improve the quality of selected feature subsets. During the feature evaluation stage, an adaptive weighting parameter is introduced to accelerate convergence and enhance feature selection efficiency. Additionally, dynamic search mechanisms are integrated using a dynamic strategy to prevent local optimization effectively. Three public network intrusion detection datasets—NSL-KDD, CIC_MalMem_2022, and RT_IOT2022—are used to evaluate the performance of the HMDOA method. Its performance is then compared with six other metaheuristic algorithms. Experimental results indicate that the HMDOA method achieves higher feature selection efficiency, faster convergence speed, and higher-quality solutions. The HMDOA method significantly improves the effect of network traffic feature selection, but the robustness of the algorithm under the background of noise and data anomalies needs to be further explored in the future.