Efficient Intrusion Detection using Weighted K-means Clustering
and Naïve Bayes Classification
Full Text |
Pdf |
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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