Probabilistic two-level anomaly detection for correlated systems

Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Idé

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

Abstract

We propose a novel probabilistic semi-supervised anomaly detection framework for multi-dimensional systems with high correlation among variables. Our method is able to identify both abnormal instances and abnormal variables of an instance.

Original languageEnglish
Title of host publicationECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
EditorsTorsten Schaub, Gerhard Friedrich, Barry O'Sullivan
PublisherIOS Press
Pages1109-1110
Number of pages2
ISBN (Electronic)9781614994183
DOIs
Publication statusPublished - Jan 1 2014
Event21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic
Duration: Aug 18 2014Aug 22 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume263
ISSN (Print)0922-6389

Other

Other21st European Conference on Artificial Intelligence, ECAI 2014
CountryCzech Republic
CityPrague
Period8/18/148/22/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Tong, B., Morimura, T., Suzuki, E., & Idé, T. (2014). Probabilistic two-level anomaly detection for correlated systems. In T. Schaub, G. Friedrich, & B. O'Sullivan (Eds.), ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings (pp. 1109-1110). (Frontiers in Artificial Intelligence and Applications; Vol. 263). IOS Press. https://doi.org/10.3233/978-1-61499-419-0-1109