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