A statistical framework for intrusion detection system

Kabir, Md Enamul and Hu, Jiankun (2014) A statistical framework for intrusion detection system. In: 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014), 19-21 Aug 2014, Xiamen, China.

Abstract

This paper proposes a statistical framework 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 defacto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 IEEE. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 29 Apr 2015 05:15
Last Modified: 23 Feb 2017 00:19
Uncontrolled Keywords: LS-SVM; intrusion detection; optimum allocation
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
10 Technology > 1005 Communications Technologies > 100503 Computer Communications Networks
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/FSKD.2014.6980966
URI: http://eprints.usq.edu.au/id/eprint/26879

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