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Anomaly detection in vessel tracking using Support Vector Machines (SVMs)

Dwi Handayani, Dini Oktarina and Sediono, Wahju and Shah, Asadullah (2014) Anomaly detection in vessel tracking using Support Vector Machines (SVMs). In: 2nd International Conference on Advanced Computer Science Applications and Technologies (ACSAT2013), 22-24 December 2013, Kuching, Sarawak, Malaysia.

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Abstract

The paper is devoted to supervise method approach to identify the vessel anomaly behavior in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behavior. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behavior. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: 6584/35362 (DOI: 10.1109/ACSAT.2013.49)ISBN: 978-147992758-6
Uncontrolled Keywords: Maritime Surveillance, AIS, SVMs, Interpolation, Anomaly Detection
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Mechatronics Engineering
Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Dr.-Ing. Wahju Sediono
Date Deposited: 14 Feb 2014 10:53
Last Modified: 24 May 2018 09:49
URI: http://irep.iium.edu.my/id/eprint/35362

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