Efficient intrusion detection scheme based on SVM

Zhou, Guangping and Shrestha, Anup (2013) Efficient intrusion detection scheme based on SVM. Journal of Networks, 8 (9). pp. 2128-2134. ISSN 1796-2056

Text (Published Version)

Download (853Kb) | Preview


The network intrusion detection problem is the focus of current academic research. In this paper, we propose to use Support Vector Machine (SVM) model to identify and detect the network intrusion problem, and simultaneously introduce a new optimization search method, referred to as Improved Harmony Search (IHS) algorithm, to determine the parameters of the SVM model for better classification accuracy. Taking the general mechanism network system of a growing city in China between 2006 and 2012 as the sample, this study divides the mechanism into normal network system and crisis network system according to the harm extent of network intrusion. We consider a crisis network system coupled with two to three normal network systems as paired samples. Experimental results show that SVMs based on IHS have a high prediction accuracy which can perform prediction and classification of network intrusion detection and assist in guarding against network intrusion.

Statistics for USQ ePrint 24067
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 ACADEMY PUBLISHER. Open Access journal. Users are free to read, download, copy, distribute, print, search, or link to the full texts of these articles.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 18 Sep 2013 23:05
Last Modified: 07 Jan 2019 02:32
Uncontrolled Keywords: support vector machine; SVM; improved harmony search; HIS; intrusion detection; comprehensive evaluation
Fields of Research : 08 Information and Computing Sciences > 0805 Distributed Computing > 080503 Networking and Communications
01 Mathematical Sciences > 0101 Pure Mathematics > 010106 Lie Groups, Harmonic and Fourier Analysis
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: 10.4304/jnw.8.9.2128-2134
URI: http://eprints.usq.edu.au/id/eprint/24067

Actions (login required)

View Item Archive Repository Staff Only