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The application of Fuzzy clustering number algorithm in network intrusion detection

机译:模糊聚类数算法在网络入侵检测中的应用

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In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.
机译:鉴于入侵检测中的K-means算法的缺陷:需要预先级簇数和敏感的初始中心,并且易于落入本地最佳,本文提出了一种模糊聚类算法。模糊规则用于表达入侵特征,并采用标准化矩阵进一步处理,以反映入侵指示符数据之间的近似度或相关程度并建立相似性矩阵。 KDD CUP1999数据集的仿真结果表明该算法具有更好的入侵检测效果,可以有效地检测网络入侵数据。

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