On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms

Kenji Yamanishi, Jun Ichi Takeuchi, Graham Williams

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

171 被引用数 (Scopus)

抄録

Outlier detection is a fundamental issue in data mining, specifically in fraud detections network intrusion detection, network monitoring, etc. SmartSifter, which we abbreviate as SS, is an outlier detection engine adrressing this problem from the viewpoint of statistical learning theory. This paper provides a theoretical basis for SS and empirically demonstrates its effectiveness. SS detects outliers in an online process through the on-line unsupervised learning of a probabilistic model (using a finite mixture model) of the information source. Each time a datum is input SS employs an on-line discounting learning algorithm to learn the probabilistic model. A score is given to the datum based on the learned model, with a high score indicating a high possibility of being a statistical outlier. The novel features of SS are: 1) it is adaptive to non-stationary sources of data; 2) a score has a clear statistical/information-theoretic meaning; 3) it is computationally inexpensive; and 4) it can handle both categorical and continuous variables. An experimental application to network intrusion detection shows that SS was able to identify data with high scores that corresponded to attacks, with low computational costs. Further experimental application has identified a number of meaningful rare cases in actual health insurance pathology data from Australia's Health Insurance Commission.

本文言語英語
ホスト出版物のタイトルProceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
編集者R. Ramakrishnan, S. Stolfo, R. Bayardo, I. Parsa
出版社Association for Computing Machinery (ACM)
ページ320-324
ページ数5
ISBN(印刷版)1581132336, 9781581132335
DOI
出版ステータス出版済み - 2000
外部発表はい
イベントProceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001) - Boston, MA, 米国
継続期間: 8 20 20008 23 2000

出版物シリーズ

名前Proceeding of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

その他

その他Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)
Country米国
CityBoston, MA
Period8/20/008/23/00

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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