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

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 so...

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Main Authors:	Dwi Handayani, Dini Oktarina, Sediono, Wahju, Shah, Asadullah
Format:	Conference or Workshop Item
Language:	English
Published:	2014
Subjects:	TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/35362/ http://irep.iium.edu.my/35362/ http://irep.iium.edu.my/35362/1/asadullah.pdf

id	iium-35362
recordtype	eprints
spelling	iium-353622018-05-24T01:49:29Z http://irep.iium.edu.my/35362/ Anomaly detection in vessel tracking using Support Vector Machines (SVMs) Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah TK5101 Telecommunication. Including telegraphy, radio, radar, television 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. 2014-12 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/35362/1/asadullah.pdf 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. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6836578&tag=1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	TK5101 Telecommunication. Including telegraphy, radio, radar, television Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
description	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.
format	Conference or Workshop Item
author	Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah
author_facet	Dwi Handayani, Dini Oktarina Sediono, Wahju Shah, Asadullah
author_sort	Dwi Handayani, Dini Oktarina
title	Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
title_short	Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
title_full	Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
title_fullStr	Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
title_full_unstemmed	Anomaly detection in vessel tracking using Support Vector Machines (SVMs)
title_sort	anomaly detection in vessel tracking using support vector machines (svms)
publishDate	2014
url	http://irep.iium.edu.my/35362/ http://irep.iium.edu.my/35362/ http://irep.iium.edu.my/35362/1/asadullah.pdf
first_indexed	2023-09-18T20:50:43Z
last_indexed	2023-09-18T20:50:43Z
_version_	1777409975380869120

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

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