高技术通讯2026，Vol.36Issue(4)：423-429,7.DOI:10.3772/j.issn.1002-0470.2026.04.009
基于并行优化与模糊聚类的入侵检测
Intrusion detection based on parallel optimization and fuzzy clustering
摘要
Abstract
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.关键词
传感器云安全/智能入侵检测/并行特征选择/离散优化/自适应模糊聚类Key words
sensor cloud security/intelligent intrusion detection/parallel feature selection/discrete optimiza-tion/adaptive fuzzy clustering引用本文复制引用
伏金娣,刘小杰,宋长新..基于并行优化与模糊聚类的入侵检测[J].高技术通讯,2026,36(4):423-429,7.基金项目
教育部中国高校产学研创新基金(2022IT230)和教育部中国高校产学研创新基金(2021LDA12008)资助项目. （2022IT230）
