FADS: a fuzzy anomaly detection system

Authors:
Dan Li;Kefei Wang;Jitender S. Deogun
Affiliations:
Department of Computer Science, Northern Arizona University, Flagstaff, AZ;Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE;Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE
Venue:
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Year:
2006

Citing 4
Cited 0

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Abstract

In this paper, we propose a novel anomaly detection framework which integrates soft computing techniques to eliminate sharp boundary between normal and anomalous behavior. The proposed method also improves data pre-processing step by identifying important features for intrusion detection. Furthermore, we develop a learning algorithm to find classifiers for imbalanced training data to avoid some assumptions made in most learning algorithms that are not necessarily sound. Preliminary experimental results indicate that our approach is very effective in anomaly detection