FADS: A fuzzy anomaly detection system

Dan Li, Kefei Wang, Jitender S. Deogun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationRough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings
PublisherSpringer Verlag
Pages792-798
Number of pages7
ISBN (Print)3540362975, 9783540362975
DOIs
StatePublished - 2006
EventFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006 - Chongqing, China
Duration: Jul 24 2006Jul 26 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4062 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceFirst International Conference on Rough Sets and Knowledge Technology, RSKT 2006
CountryChina
CityChongqing
Period7/24/067/26/06

Keywords

  • Anomaly detection
  • Data mining
  • Fuzzy theory

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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