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A bayesian parametric statistical anomaly detection method for finding trends and patterns in criminal behavior
RISE, Swedish ICT, SICS.ORCID iD: 0000-0001-8577-6745
RISE, Swedish ICT, SICS.ORCID iD: 0000-0002-6797-8463
2013 (English)In: Proceedings - 2013 European Intelligence and Security Informatics Conference, EISIC 2013, 2013, p. 83-88Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we describe how Bayesian Principal Anomaly Detection (BPAD) can be used for detecting long and short term trends and anomalies in geographically tagged alarm data. We elaborate on how the detection of such deviations can be used for high-lighting suspected criminal behavior and activities. BPAD has previously been successively deployed and evaluated in several similar domains, including Maritime Domain Awareness, Train Fleet Maintenance, and Alarm filtering. Similar as for those applications, we argue in the paper that the deployment of BPAD in area of crime monitoring potentially can improve the situation awareness of criminal activities, by providing automatic detection of suspicious behaviors, and uncovering large scale patterns.

Place, publisher, year, edition, pages
2013. p. 83-88
Keywords [en]
Anomaly detection, Bayesian statistics, Criminal behaviour, Situation awareness, Automatic Detection, Criminal activities, Maritime domain awareness, Statistical anomaly detection, Fleet operations, Information science, Crime
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-48721DOI: 10.1109/EISIC.2013.19Scopus ID: 2-s2.0-84892155235ISBN: 9780769550626 (print)OAI: oai:DiVA.org:ri-48721DiVA, id: diva2:1468089
Conference
2013 4th European Intelligence and Security Informatics Conference, EISIC 2013, 12 August 2013 through 14 August 2013, Uppsala
Available from: 2020-09-17 Created: 2020-09-17 Last updated: 2023-05-09Bibliographically approved

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Holst, AndersBjurling, Björn

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Citation style
  • apa
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Language
  • de-DE
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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