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A formal approach to anomaly detection
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-5750-9655
2016 (English)In: ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, SciTePress, 2016, 317-326 p.Conference paper (Refereed)
Abstract [en]

While many advances towards effective anomaly detection techniques targeting specific applications have been made in recent years, little work has been done to develop application-agnostic approaches to the subject. In this article, we present such an approach, in which anomaly detection methods are treated as formal, structured objects. We consider a general class of methods, with an emphasis on methods that utilize structural properties of the data they operate on. For this class of methods, we develop a decomposition into sub-methods-simple, restricted objects, which may be reasoned about independently and combined to form methods. As we show, this formalism enables the construction of software that facilitates formulating, implementing, evaluating, as well as algorithmically finding and calibrating anomaly detection methods.

Place, publisher, year, edition, pages
SciTePress, 2016. 317-326 p.
Keyword [en]
Anomaly detection, Formal methods, Model selection, Pattern recognition, Signal detection, Anomaly detection methods, Class of methods, Formal approach, General class
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-195434ScopusID: 2-s2.0-84970003840ISBN: 9789897581731 (print)OAI: oai:DiVA.org:kth-195434DiVA: diva2:1047768
Conference
5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016, 24 February 2016 through 26 February 2016
Note

QC 20161118

Available from: 2016-11-18 Created: 2016-11-03 Last updated: 2016-11-18Bibliographically approved

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