Affiliations: [a] College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta GA 30332-0280, USA. E-mail: [email protected] | [b] IBM T.J. Watson Research Center, Hawthorne, NY 10532, USA. E-mail: [email protected] | [c] Computer Science Department, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA. E-mail: [email protected], [email protected] | [d] Computer Science Department, State University of New York at Stony Brook, Stony Brook, NY 11794-4400, USA. E-mail: [email protected]
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Corresponding author.
Abstract: Intrusion detection systems (IDSs) must maximize the realization of security goals while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models. We examine the major cost factors associated with an IDS, which include development cost, operational cost, damage cost due to successful intrusions, and the cost of manual and automated response to intrusions. These cost factors can be qualified according to a defined attack taxonomy and site-specific security policies and priorities. We define cost models to formulate the total expected cost of an IDS, and present cost-sensitive machine learning techniques that can produce detection models that are optimized for user-defined cost metrics. Empirical experiments show that our cost-sensitive modeling and deployment techniques are effective in reducing the overall cost of intrusion detection.
