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Journal of Emerging Trends in Computing and Information Sciences >> Call for Papers Vol. 8 No. 3, March 2017

Journal of Emerging Trends in Computing and Information Sciences

Efficient Intrusion Detection using Weighted K-means Clustering and Naïve Bayes Classification

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Author Yousef Emami, Marzieh Ahmadzadeh, Mohammad Salehi, Sajad Homayoun
ISSN 2079-8407
On Pages 620-623
Volume No. 5
Issue No. 8
Issue Date September 1, 2014
Publishing Date September 1, 2014
Keywords Intrusion Detection System, K-means Clustering, Naïve Bayes Classification


Abstract

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.
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