Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors

Andriy Enttsel; Filippo Martinini; Alex Marchioni; Mauro Mangia; Riccardo Rovatti; Gianluca Setti

doi:10.1109/ICASSP49357.2023.10095287

Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors

Andriy Enttsel^*, Filippo Martinini^*, Alex Marchioni^*, Mauro Mangia^*, Riccardo Rovatti^*, Gianluca Setti

^*Corresponding author for this work

Electrical and Computer Engineering

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

3 Scopus citations

Abstract

Anomaly Detection is a challenging task due to the limited knowledge about possible anomalies. This issue can be tackled by modeling anomalies through domain expertise or collecting sufficient anomalous data. However, some domains, such as monitoring systems, require detectors that are capable of detecting any potential alteration in the observed phenomenon. Hereby we propose a tool to generate anomalies as a statistical deviation from the characterization of the signal representing the normal behavior. Two families of deviation models are presented, and the effectiveness of the tool is proven using well-known unsupervised detectors. The effects of a possible intermediate data compression stage on the detection capabilities are also considered.

Original language	English (US)
Title of host publication	ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728163277
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10095287
State	Published - 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: Jun 4 2023 → Jun 10 2023

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2023-June
ISSN (Print)	1520-6149

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	06/4/23 → 06/10/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

anomaly sets
lossy compression
Outlier detection
principal component analysis

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP49357.2023.10095287

Cite this

Enttsel, A., Martinini, F., Marchioni, A., Mangia, M., Rovatti, R., & Setti, G. (2023). Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP49357.2023.10095287

Enttsel, Andriy ; Martinini, Filippo ; Marchioni, Alex et al. / Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors",

abstract = "Anomaly Detection is a challenging task due to the limited knowledge about possible anomalies. This issue can be tackled by modeling anomalies through domain expertise or collecting sufficient anomalous data. However, some domains, such as monitoring systems, require detectors that are capable of detecting any potential alteration in the observed phenomenon. Hereby we propose a tool to generate anomalies as a statistical deviation from the characterization of the signal representing the normal behavior. Two families of deviation models are presented, and the effectiveness of the tool is proven using well-known unsupervised detectors. The effects of a possible intermediate data compression stage on the detection capabilities are also considered.",

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author = "Andriy Enttsel and Filippo Martinini and Alex Marchioni and Mauro Mangia and Riccardo Rovatti and Gianluca Setti",

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year = "2023",

doi = "10.1109/ICASSP49357.2023.10095287",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Enttsel, A, Martinini, F, Marchioni, A, Mangia, M, Rovatti, R & Setti, G 2023, Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors. in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June, Institute of Electrical and Electronics Engineers Inc., 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 06/4/23. https://doi.org/10.1109/ICASSP49357.2023.10095287

Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors. / Enttsel, Andriy; Martinini, Filippo; Marchioni, Alex et al.
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2023-June).

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

TY - GEN

T1 - Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors

AU - Enttsel, Andriy

AU - Martinini, Filippo

AU - Marchioni, Alex

AU - Mangia, Mauro

AU - Rovatti, Riccardo

AU - Setti, Gianluca

PY - 2023

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N2 - Anomaly Detection is a challenging task due to the limited knowledge about possible anomalies. This issue can be tackled by modeling anomalies through domain expertise or collecting sufficient anomalous data. However, some domains, such as monitoring systems, require detectors that are capable of detecting any potential alteration in the observed phenomenon. Hereby we propose a tool to generate anomalies as a statistical deviation from the characterization of the signal representing the normal behavior. Two families of deviation models are presented, and the effectiveness of the tool is proven using well-known unsupervised detectors. The effects of a possible intermediate data compression stage on the detection capabilities are also considered.

AB - Anomaly Detection is a challenging task due to the limited knowledge about possible anomalies. This issue can be tackled by modeling anomalies through domain expertise or collecting sufficient anomalous data. However, some domains, such as monitoring systems, require detectors that are capable of detecting any potential alteration in the observed phenomenon. Hereby we propose a tool to generate anomalies as a statistical deviation from the characterization of the signal representing the normal behavior. Two families of deviation models are presented, and the effectiveness of the tool is proven using well-known unsupervised detectors. The effects of a possible intermediate data compression stage on the detection capabilities are also considered.

KW - anomaly sets

KW - lossy compression

KW - Outlier detection

KW - principal component analysis

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DO - 10.1109/ICASSP49357.2023.10095287

M3 - Conference contribution

AN - SCOPUS:85177605598

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

BT - ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

Y2 - 4 June 2023 through 10 June 2023

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Enttsel A, Martinini F, Marchioni A, Mangia M, Rovatti R, Setti G. Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP49357.2023.10095287

Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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