A Multiple Model Cost-Sensitive Approach for Intrusion Detection
Abstract
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 offline 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.
Cite
Text
Fan et al. "A Multiple Model Cost-Sensitive Approach for Intrusion Detection." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_15Markdown
[Fan et al. "A Multiple Model Cost-Sensitive Approach for Intrusion Detection." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/fan2000ecml-multiple/) doi:10.1007/3-540-45164-1_15BibTeX
@inproceedings{fan2000ecml-multiple,
title = {{A Multiple Model Cost-Sensitive Approach for Intrusion Detection}},
author = {Fan, Wei and Lee, Wenke and Stolfo, Salvatore J. and Miller, Matthew},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {142-153},
doi = {10.1007/3-540-45164-1_15},
url = {https://mlanthology.org/ecmlpkdd/2000/fan2000ecml-multiple/}
}