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Hybrid machine learning technique for intrusion detection system

Mohamad Tahir, Hatim and Hasan, Wail and Md Said, Abas and Zakaria, Nur Haryani and Katuk, Norliza and Kabir, Nur Farzana and Omar, Mohd Hasbullah and Ghazali, Osman and Yahya, Noor Izzah (2015) Hybrid machine learning technique for intrusion detection system. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey.

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The utilization of the Internet has grown tremendously resulting in more critical data are being transmitted and handled online.Hence, these occurring changes have led to draw the conclusion that thenumber of attacks on the important information over the internet is increasing yearly.Intrusion is one of the main threat to the internet.Various techniques and approaches have been developed to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This research proposed a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and support vector machine classification.The aim of this research is to reduce the rate of false positive alarm, false negative alarm rate and to improve the detection rate.The NSL-KDD dataset has been used in the proposed technique.In order to improve classification performance, some steps have been taken on the dataset.The classification has been performed by using support vector machine. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique has achieved a positive detection rate and reduce the false alarm rate.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN No: 978-967-0910-02-4 Jointly organized by: Universiti Utara Malaysia & Istanbul Zaim University
Uncontrolled Keywords: intrusion detection, hybrid intelligent technique, K-means, SVM, NSL-KDD
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: School of Computing
Depositing User: Prof. Madya Hatim Mohamad Tahir
Date Deposited: 01 Oct 2015 07:58
Last Modified: 27 Apr 2016 06:48
URI: http://repo.uum.edu.my/id/eprint/15600

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