A novel statistical technique for intrusion detection systems

Kabir, Enamul and Hu, Jiankun and Wang, Hua and Zhuo, Guangping (2017) A novel statistical technique for intrusion detection systems. Future Generations Computer Systems. ISSN 0167-739X

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Abstract

This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes and multiclass are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. Finally a way out is also shown the usability of the proposed algorithm for incremental datasets.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: First place winner of the USQ Publication Excellence Award for Journal Articles published Jan-March 2017. Accepted version deposited, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 13 Feb 2017 02:38
Last Modified: 11 Apr 2018 05:22
Uncontrolled Keywords: sampling, Intrusion Detection System (IDS), network security, least, Square Support Vector Machine (LS-SVM)
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Identification Number or DOI: 10.1016/j.future.2017.01.029
URI: http://eprints.usq.edu.au/id/eprint/30388

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