カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2014/12/01タイトル(英語): Classification with Clustering and Gaussian Functions in Intrusion Detection System著者名: Nannan Lu (School of Information and Electrical Engineering, China University of Mining and Technology), Shingo Mabu (Graduate School of information, Production and Systems, Waseda University), Yuhong Li (Graduate School of information, Production and Sys著者名(英語): Nannan Lu (School of Information and Electrical Engineering, China University of Mining and Technology), Shingo Mabu (Graduate School of information, Production and Systems, Waseda University), Yuhong Li (Graduate School of information, Production and Systems, Waseda University), Kotaro Hirasawa (Graduate School of information, Production and Systems, Waseda University)キーワード: Intrusion Detection System，Average Matching Degree，Gaussian Function，Clustering要約(英語): Efficient classification plays a significant role in rule-based Intrusion Detection Systems. In order to make full use of the information in the rule pool, in this paper, a novel approach has been proposed to improve the detection performance by building a Gaussian function for each cluster in the two-dimensional average matching degree space, instead of analyzing the distance in the two-dimensional average matching degree space. A clustering method is also proposed which calculates the number of clusters and their centers depending on the crowdness of the points of each class. Considering the importance of the number of clusters, the performance of the intrusion detection is evaluated by changing the size of clusters. Simulation results show that the proposed approach based on the Gaussian function of each cluster is effective and efficient for distinguishing normal, misuse and anomaly intrusions.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.12 （2014） 特集：電気関係学会東海支部連合大会本誌掲載ページ: 1908-1915 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/134/12/134_1908/_article/-char/ja/
