Yassin, Warusia and Udzir, Nur Izura and Muda, Zaiton and Sulaiman, Md Nasir (2013) Anomaly-based intrusion detection through K-Means clustering and Naives Bayes classification. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28 -30 August 2013, Kuching, Sarawak, Malaysia.

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Abstract

Intrusion detection systems (IDSs) effectively balance extra security appliance by identifying intrusive activities on a computer system, and their enhancement is emerging at an unexpected rate.Anomaly-based intrusion detection methods, which employ machine learning algorithms, are able to identify unforeseen attacks. Regrettably, the foremost challenge of this method is to minimize false alarm while maximizing detection and accuracy rate.We propose an integrated machine learning algorithm across K-Mean s clustering and Naïve Bayes Classifier called KMC+NBC to overcome the aforesaid drawbacks.K-Means clustering is applied to labeling and gathers the entire data into corresponding cluster sets based on the data behavior,i.e.,i.e. normal and attack, while Naïve Bayes Classifier (NBC) is applied to reorder the misclassified clustered data into correct categories. Experiments have been performed to evaluate the performance of KMC+NBC and NBC against ISCX 2012 Intrusion Detection Evaluation Dataset.The result shows that KMC+NBC significantly improves the accuracy, detection rate up to 99% and 98.8%, respectively, while decreasing the false alarm to 2.2%

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	ISBN: 9789832078791 Organized by: Universiti Utara Malaysia
Uncontrolled Keywords:	Intrusion Detection System, Anomaly-based Intrusion Detection, Machine Learning, Clustering, Classifier
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	25 Aug 2014 06:58
Last Modified:	25 Aug 2014 06:58
URI:	https://repo.uum.edu.my/id/eprint/12029

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