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Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach

Handayani, Dini and Sediono, Wahju (2015) Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach. International Journal of Maritime Engineering (RINA Transactions Part A), 157 (A3). pp. 145-152. ISSN 1479-8751

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The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

Item Type: Article (Journal)
Additional Information: 6584/42799
Uncontrolled Keywords: vessel tracking, Bayesian Networks (BNs)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
V Naval Science > V Naval Science (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr.-Ing. Wahju Sediono
Date Deposited: 25 May 2015 15:56
Last Modified: 11 Mar 2016 18:06
URI: http://irep.iium.edu.my/id/eprint/42799

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