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General Information
    • ISSN: 1793-8244 (Print)
    • Abbreviated Title:  J. Adv. Comput. Netw.
    • Frequency: Semiyearly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Professor Haklin Kimm
    • Executive Editor: Ms. Cherry Chan
    • Abstracting/ Indexing: EBSCO, ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
Professor Haklin Kimm
East Stroudsburg University, USA
I'm happy to take on the position of editor in chief of JACN. We encourage authors to submit papers on all aspects of computer networks.

JACN 2014 Vol.2(1): 85-88 ISSN: 1793-8244
DOI: 10.7763/JACN.2014.V2.87

Analysis of the Effect of Clustering the Training Data in Naive Bayes Classifier for Anomaly Network Intrusion Detection

Uma Subramanian and Hang See Ong

Abstract—This paper presents the analysis of the effect of clustering the training data and test data in classification efficiency of Naive Bayes classifier. KDD cup 99 benchmark dataset is used in this research. The training set is clustered using k means clustering algorithm into 5 clusters. Then 8800 samples are taken from the clusters to form the training and test set. The results are compared with that of two Naive Bayes classifiers trained on random sampled data containing 8800 and 17600 instances respectively. The main contribution of this paper is that it is empirically proved that the training set derived from clusters generated by k-means clustering algorithm improves the classification efficiency of the Naive Bayes classifier. The results show the accuracy of the Naive Bayes classifier trained with clustered instances is 94.4% while that of normal instances are 85.41% and 89.26%.

Index Terms—Network security, machine learning, classifier evaluation, anomaly intrusion detection.

Uma Subramanian and Hang See Ong are with the Department of Electronics and Communication Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM- UNITEN, Kajang, Selangor, 43000, Malaysia (Corresponding author: Uma Subramanian, e-mail: umas746@gmail.com).


Cite:Uma Subramanian and Hang See Ong, "Analysis of the Effect of Clustering the Training Data in Naive Bayes Classifier for Anomaly Network Intrusion Detection," Journal of Advances in Computer Networks vol. 2, no. 1, pp. 85-88, 2014.

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