基于并行优化与模糊聚类的入侵检测OA

This paper proposes an intrusion detection framework for sensor-cloud environments that integrates parallel dis-crete optimization with machine learning to enhance system security.Firstly,an optimal feature evaluation criterion is established,and a parallel discrete optimization-based feature extraction system is developed to reduce dimen-sionality and improve feature stability.Secondly,an intelligent iterative evolutionary strategy with global conver-gence is incorporated to efficiently obtain the optimal feature subset.Finally,a self-adaptive distributed fuzzy clus-tering method is employed to analyze the extracted features,automatically determining the number of clusters and alleviating local optima,thereby enabling accurate intrusion detection.Experimental results show that the proposed method achieves higher detection accuracy,a lower missed detection rate,and stable performance in noisy environ-ments,demonstrating its strong robustness.