Toward Cost-Sensitive Modeling for Intrusion Detection
- Toward Cost-Sensitive Modeling for Intrusion Detection
- Lee, Wenke
Park, Christopher T.
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- 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.
- Computer science
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- Suggested Citation:
- Wenke Lee, Matthew Miller, Salvatore Stolfo, Kahil Jallad, Christopher T. Park, Erez Zadok, Vijay Prabhakar, 2000, Toward Cost-Sensitive Modeling for Intrusion Detection, Columbia University Academic Commons, https://doi.org/10.7916/D8RJ4WQ2.