An Online Stream Monitoring Algorithm for Fraud Detection in the Transport of Goods

• Autorzy: Białoń Paweł M.

Identyfikatory

DOI

10.26636/jtit.2020.146620

• Języki publikacji: EN

Abstrakty

EN

The process of monitoring vehicles used in road transports plays an important role in detecting fraud committed by drivers. Algorithm designers face a number of challenges, including large number of vehicles monitored, demands related to online calculations, and ability to easily explain fraud alarms triggered to supervisors who make final decisions about actions to be taken. In this paper, we propose rather general, lightweight stream, online heuristics. The vehicle’s position is periodically controlled by a GNSS device. The algorithm detects potential illegal activities along the route between the origin and the destination. Anomalies in the vehicle’s trajectory are detected, based on a multi-resolution analysis of the economy of routes. The economy metric is easily understood and verifiable by controllers. The solution is also capable of identifying clearly suspicious trajectories that popular geofencing approaches would overlook. The scale on which the solution may be adopted is obtained thanks to the stream – like nature of the algorithm: essentially, the resources used do not increase along with the size of the input stream (the number of GNSS frames generated for the vehicle). An experiment illustrating the algorithm’s viability is presented as well.

Słowa kluczowe

EN

fraud detection; stream processing; transport monitoring

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2020
• Tom: nr 4
• Strony: 79--85
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Białoń Paweł M.

• Advanced Information Technologies Department, National Institute of Telecommunications, Szachowa 1, 04-894 Warsaw, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).