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.
CITATION STYLE
Lee, W., Fan, W., Stolfo, S. J., & Miller, M. (2006). Cost-Sensitive Modeling for Intrusion Detection. In Machine Learning and Data Mining for Computer Security (pp. 125–136). Springer-Verlag. https://doi.org/10.1007/1-84628-253-5_8
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