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Toward Cost-Sensitive Modeling for Intrusion Detection

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

Title:
Toward Cost-Sensitive Modeling for Intrusion Detection
Author(s):
Lee, Wenke
Miller, Matthew
Stolfo, Salvatore
Jallad, Kahil
Park, Christopher T.
Zadok, Erez
Prabhakar, Vijay
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-002-00
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
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.
Subject(s):
Computer science
Item views:
131
Metadata:
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