文章基本信息

标题：Efficient Intrusion Detection using Weighted K-means Clustering and Naïve Bayes Classification
本地全文：下载
作者：Yousef Emami ; Marzieh Ahmadzadeh ; Mohammad Salehi 等
期刊名称：Journal of Emerging Trends in Computing and Information Sciences
电子版ISSN：2079-8407
出版年度：2014
卷号：5
期号：8
页码：620-623
出版社：ARPN Publishers
摘要：Intrusion detection system (IDS) is becoming a vital component to secure the network. A successful intrusion detection system requires high accuracy and detection rate. In this paper a hybrid approach for intrusion detection system based on data mining techniques is proposed. The principal ingredients of the approach are weighted k-means clustering and naive bayes classification. The C5.0 algorithm is used for ranking attributes, so the attributes receive a weight which is used in K-means clustering therefore accuracy of clustering is increased.
关键词：Intrusion Detection System; K-means Clustering; Naïve Bayes Classification