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- W1569698647 abstract "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." @default.
- W1569698647 created "2016-06-24" @default.
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- W1569698647 date "2000-01-01" @default.
- W1569698647 modified "2023-09-23" @default.
- W1569698647 title "Toward Cost-Sensitive Modeling for Intrusion Detection" @default.
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- W1569698647 doi "https://doi.org/10.7916/d8rj4wq2" @default.
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