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On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation

Singh, Sandeep Kumar und Heymann, Frank (2020) On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation. In: 23rd International Conference on Information Fusion, FUSION 2020. International Conference on Information Fusion, 2020-07-06 - 2020-07-09, Virtual (online). doi: 10.23919/FUSION45008.2020.9190533. ISBN 978-057864709-8.

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Kurzfassung

The automatic identification system (AIS) has become an essential tool for maritime security. Nevertheless, how to effectively use the static and dynamic voyage information of the AIS data in maritime traffic situation awareness is still a challenge. This paper presents a comparative study of artificial intelligence (AI) techniques on their effectiveness in dealing with various anomalies in maritime domain using the AIS data. The AIS on-off switching (OOS) anomaly is critical in maritime security, since AIS technology is susceptible to manipulation and it can be switched on and off to hide illegal activities. Thus, we try to detect and distinguish between intentional and nonintentional AIS OOS anomalies through our AI-assisted anomaly detection framework. We use AIS data, in particular positional and navigational status of vessels, to study the effectiveness of seven AI techniques, such as artificial neural network, support vector machine, logistic regression, k-nearest neighbors, decision tree, random forest and naive Bayes, in detecting the AIS OOS anomalies. Our experimental results show that ANN and SVM are the most suitable techniques in detecting the AIS OOS anomalies with 99.9% accuracy. Interestingly, the ANN model outperforms others when trained with a balanced (i.e., same order of samples per class) dataset, and SVM, on the other hand, is suitable when training dataset is unbalanced.

elib-URL des Eintrags:https://elib.dlr.de/135456/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Singh, Sandeep KumarSandeep.Singh (at) dlr.dehttps://orcid.org/0000-0002-8734-9832NICHT SPEZIFIZIERT
Heymann, FrankFrank.Heymann (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Juli 2020
Erschienen in:23rd International Conference on Information Fusion, FUSION 2020
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI:10.23919/FUSION45008.2020.9190533
ISBN:978-057864709-8
Status:veröffentlicht
Stichwörter:Maritime security, anomaly detection, machine learning
Veranstaltungstitel:International Conference on Information Fusion
Veranstaltungsort:Virtual (online)
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:6 Juli 2020
Veranstaltungsende:9 Juli 2020
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Verkehrssystem
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VS - Verkehrssystem
DLR - Teilgebiet (Projekt, Vorhaben):V - I4Port (alt)
Standort: Neustrelitz
Institute & Einrichtungen:Institut für Kommunikation und Navigation > Nautische Systeme
Hinterlegt von: Singh, Sandeep Kumar
Hinterlegt am:16 Jul 2020 18:18
Letzte Änderung:24 Apr 2024 20:38

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