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

Probabilistic two-level anomaly detection for correlated systems. / Tong, Bin; Morimura, Tetsuro; Suzuki, Einoshin; Idé, Tsuyoshi.

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 proceedingConference 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). https://doi.org/10.3233/978-1-61499-419-0-1109
Tong, Bin ; Morimura, Tetsuro ; Suzuki, Einoshin ; Idé, Tsuyoshi. / 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).
@inproceedings{d2cdf7c50d88482caedcbf5b41365964,
title = "Probabilistic two-level anomaly detection for correlated systems",
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.",
author = "Bin Tong and Tetsuro Morimura and Einoshin Suzuki and Tsuyoshi Id{\'e}",
year = "2014",
month = "1",
day = "1",
doi = "10.3233/978-1-61499-419-0-1109",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "1109--1110",
editor = "Torsten Schaub and Gerhard Friedrich and Barry O'Sullivan",
booktitle = "ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings",
address = "Netherlands",

}

TY - GEN

T1 - Probabilistic two-level anomaly detection for correlated systems

AU - Tong, Bin

AU - Morimura, Tetsuro

AU - Suzuki, Einoshin

AU - Idé, Tsuyoshi

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84923171833&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923171833&partnerID=8YFLogxK

U2 - 10.3233/978-1-61499-419-0-1109

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

M3 - Conference contribution

AN - SCOPUS:84923171833

T3 - Frontiers in Artificial Intelligence and Applications

SP - 1109

EP - 1110

BT - ECAI 2014 - 21st European Conference on Artificial Intelligence, Including Prestigious Applications of Intelligent Systems, PAIS 2014, Proceedings

A2 - Schaub, Torsten

A2 - Friedrich, Gerhard

A2 - O'Sullivan, Barry

PB - IOS Press

ER -