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Toward Cost-Sensitive Modeling for Intrusion Detection

Lee, Wenke; Miller, Matthew; Stolfo, Salvatore; Jallad, Kahil; Park, Christopher T.; Zadok, Erez; Prabhakar, Vijay

Intrusion detection systems need to maximize security while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models. We examine the major cost factors: development costs, operational costs, damage costs incurred due to intrusions, and the costs involved in responding to intrusions. We propose cost-sensitive machine learning techniques to produce models that are optimized for user-defined cost metrics. We describe an automated approach for generating efficient run-time versions of these models. Empirical experiments in off-line analysis and real-time detection show that our cost-sensitive modeling and deployment techniques are effective in reducing the overall cost of intrusion detection.

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Academic Units
Computer Science
Publisher
Department of Computer Science, Columbia University
Series
Columbia University Computer Science Technical Reports, CUCS-002-00
Published Here
April 22, 2011
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