MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
A data mining framework for constructing features and models for intrusion detection systems (computer security, network security)
Bro: a system for detecting network intruders in real-time
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Intrusion detection systems (IDSs) need to maximize security while minimizing costs. In this paper, we study the problem of building cost-sensitive intrusion detection models to be used for real-time detection. We briefly discuss the major cost factors in IDS, including consequential and operational costs. We propose a multiple model cost-sensitive machine learning technique to produce models that are optimized for user-defined cost metrics. Empirical experiments in off-line analysis show a reduction of approximately 97% in operational cost over a single model approach, and a reduction of approximately 30% in consequential cost over a pure accuracy-based approach.