@inproceedings{9a00116fce064ab4a026d5b2f2d466cb,
title = "FADS: A fuzzy anomaly detection system",
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.",
keywords = "Anomaly detection, Data mining, Fuzzy theory",
author = "Dan Li and Kefei Wang and Deogun, {Jitender S.}",
year = "2006",
doi = "10.1007/11795131_115",
language = "English (US)",
isbn = "3540362975",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "792--798",
booktitle = "Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings",
note = "First International Conference on Rough Sets and Knowledge Technology, RSKT 2006 ; Conference date: 24-07-2006 Through 26-07-2006",
}