Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach
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 ha...
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Transactions of The Royal Institution of Naval Architects.
2015
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| Online Access: | http://irep.iium.edu.my/42799/ http://irep.iium.edu.my/42799/ http://irep.iium.edu.my/42799/1/46.ANOMALY_DETECTION_BAYESIAN_NETWORKS_APPROACH.ijme.2015.pdf http://irep.iium.edu.my/42799/4/42799_anomaly_detection_in_vessel_tracking_Scopus.pdf |
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iium-427992016-03-11T10:06:20Z http://irep.iium.edu.my/42799/ Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach Handayani, Dini Sediono, Wahju TK7885 Computer engineering V Naval Science (General) 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. Transactions of The Royal Institution of Naval Architects. 2015 Article NonPeerReviewed application/pdf en http://irep.iium.edu.my/42799/1/46.ANOMALY_DETECTION_BAYESIAN_NETWORKS_APPROACH.ijme.2015.pdf application/pdf en http://irep.iium.edu.my/42799/4/42799_anomaly_detection_in_vessel_tracking_Scopus.pdf 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 http://www.rina.org.uk/ijme.html |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
TK7885 Computer engineering V Naval Science (General) |
| spellingShingle |
TK7885 Computer engineering V Naval Science (General) Handayani, Dini Sediono, Wahju Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| description |
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. |
| format |
Article |
| author |
Handayani, Dini Sediono, Wahju |
| author_facet |
Handayani, Dini Sediono, Wahju |
| author_sort |
Handayani, Dini |
| title |
Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| title_short |
Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| title_full |
Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| title_fullStr |
Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| title_full_unstemmed |
Anomaly detection in vessel tracking: a Bayesian Networks (BNs) approach |
| title_sort |
anomaly detection in vessel tracking: a bayesian networks (bns) approach |
| publisher |
Transactions of The Royal Institution of Naval Architects. |
| publishDate |
2015 |
| url |
http://irep.iium.edu.my/42799/ http://irep.iium.edu.my/42799/ http://irep.iium.edu.my/42799/1/46.ANOMALY_DETECTION_BAYESIAN_NETWORKS_APPROACH.ijme.2015.pdf http://irep.iium.edu.my/42799/4/42799_anomaly_detection_in_vessel_tracking_Scopus.pdf |
| first_indexed |
2023-09-18T21:01:00Z |
| last_indexed |
2023-09-18T21:01:00Z |
| _version_ |
1777410622888083456 |