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

Singh, Sandeep Kumar and 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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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.

Item URL in elib:https://elib.dlr.de/135456/
Document Type:Conference or Workshop Item (Speech)
Title:On the Effectiveness of AI-Assisted Anomaly Detection Methods in Maritime Navigation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Singh, Sandeep KumarUNSPECIFIEDhttps://orcid.org/0000-0002-8734-9832UNSPECIFIED
Date:July 2020
Journal or Publication Title:23rd International Conference on Information Fusion, FUSION 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Maritime security, anomaly detection, machine learning
Event Title:International Conference on Information Fusion
Event Location:Virtual (online)
Event Type:international Conference
Event Start Date:6 July 2020
Event End Date:9 July 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - I4Port (old)
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Singh, Sandeep Kumar
Deposited On:16 Jul 2020 18:18
Last Modified:24 Apr 2024 20:38

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