A novel anomaly detection procedure based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity. In order to hide this behavior, the vessel switches off its automatic identification system (AIS) device for a certain time T and then tries to revert to the previous nominal velocity. The decision that has to be made is declaring that a deviation either happened or not, relying only upon two consecutive AIS contacts. Furthermore, the extension to the scenario in which multiple contacts (e.g., radar) are available during the time period T is also considered. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter of the vessel is the core of the proposed approach and enables the solution of the anomaly detection problem. Closed analytical forms are provided for the detection and false alarm probabilities of the hypothesis test.

Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein–Uhlenbeck Process / Enrica d'Afflisio. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - ELETTRONICO. - 66:(2018), pp. 6474-6487. [10.1109/TSP.2018.2875887]

Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein–Uhlenbeck Process

D'AFFLISIO, ENRICA
2018

Abstract

A novel anomaly detection procedure based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity. In order to hide this behavior, the vessel switches off its automatic identification system (AIS) device for a certain time T and then tries to revert to the previous nominal velocity. The decision that has to be made is declaring that a deviation either happened or not, relying only upon two consecutive AIS contacts. Furthermore, the extension to the scenario in which multiple contacts (e.g., radar) are available during the time period T is also considered. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter of the vessel is the core of the proposed approach and enables the solution of the anomaly detection problem. Closed analytical forms are provided for the detection and false alarm probabilities of the hypothesis test.
2018
66
6474
6487
Enrica d'Afflisio
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1178812
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