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

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 de...

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Bibliographic Details
Main Authors: Enttsel, Andriy, Martinini, Filippo, Marchioni, Alex, Mangia, Mauro, Rovatti, Riccardo, Setti, Gianluca
Format: Conference Proceeding
Language:English
Subjects:
anomaly sets
Behavioral sciences
Data compression
Data models
Detectors
Electrocardiography
lossy compression
Outlier detection
principal component analysis
Signal processing
Task analysis
Online Access:Request full text
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095287