Title: Identifying network intrusion with defensive forecasting

Authors: Heechang Shin

Addresses: Iona College, Hagan School of Business, New Rochelle, NY 10801, USA

Abstract: With tremendous growth of computing devices connected to networks, information systems security has become an issue of serious global concern. Various researches reveal that many organisations reported computer security breaches, and financial losses due to the security breach will be significant considering the fact that the financial losses on a per-incident basis are estimated 0.5% to 1% of annual sales. Developing effective methods for the prevention and detection of network intrusions is essential. In this paper, a methodology using game theoretic model, called defensive forecasting, is presented for real-time detection of intrusions. Experimental results show that the proposed approach is as good as or better than the previously proposed approaches.

Keywords: network intrusion detection; data mining; classification; defensive forecasting; network security; support vector machines; SVM; game theory.

DOI: 10.1504/IJBCRM.2011.041487

International Journal of Business Continuity and Risk Management, 2011 Vol.2 No.2, pp.91 - 104

Available online: 24 Jul 2011 *

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