Probabilistic two-level anomaly detection for correlated systems

Bin Tong; Tetsuro Morimura; Einoshin Suzuki; Tsuyoshi Idé

doi:10.3233/978-1-61499-419-0-1109

Probabilistic two-level anomaly detection for correlated systems

Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Idé

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings
Editors	Torsten Schaub, Gerhard Friedrich, Barry O'Sullivan
Publisher	IOS Press
Pages	1109-1110
Number of pages	2
ISBN (Electronic)	9781614994183
DOIs	https://doi.org/10.3233/978-1-61499-419-0-1109
Publication status	Published - 2014
Event	21st European Conference on Artificial Intelligence, ECAI 2014 - Prague, Czech Republic Duration: Aug 18 2014 → Aug 22 2014

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	263
ISSN (Print)	0922-6389

Other

Other	21st European Conference on Artificial Intelligence, ECAI 2014
Country/Territory	Czech Republic
City	Prague
Period	8/18/14 → 8/22/14

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Access to Document

10.3233/978-1-61499-419-0-1109

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

Probabilistic two-level anomaly detection for correlated systems. / Tong, Bin; Morimura, Tetsuro; Suzuki, Einoshin et al.
ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. ed. / Torsten Schaub; Gerhard Friedrich; Barry O'Sullivan. IOS Press, 2014. p. 1109-1110 (Frontiers in Artificial Intelligence and Applications; Vol. 263).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Frontiers in Artificial Intelligence and Applications, vol. 263, IOS Press, pp. 1109-1110, 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic, 8/18/14. https://doi.org/10.3233/978-1-61499-419-0-1109

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

Tong, Bin ; Morimura, Tetsuro ; Suzuki, Einoshin et al. / Probabilistic two-level anomaly detection for correlated systems. ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings. editor / Torsten Schaub ; Gerhard Friedrich ; Barry O'Sullivan. IOS Press, 2014. pp. 1109-1110 (Frontiers in Artificial Intelligence and Applications).

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Probabilistic two-level anomaly detection for correlated systems

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