Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

Ran Yan; Shuaian Wang

doi:10.1007/978-981-13-8683-1_4

Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

Ran Yan, Shuaian Wang^*

^*Corresponding author for this work

Hong Kong Polytechnic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

Maritime safety and security are gaining increasing concern in recent years. There are a growing number of studies aiming at improving situational awareness in the maritime domain by identifying vessel anomaly behaviors based on the data provided by the Automatic Identification System (AIS). Two types of data-driven methods are most popular in vessel anomaly detection based on AIS data: the statistical methods and machine learning methods. To improve the detection model efficiency and accuracy, hybrid models are formed by combining different types of methods. In order to incorporate expert knowledge, interactive systems are also designed and realized. In this paper, we provide a review of the popular statistical and machine learning models, as well as the hybrid models and interactive systems based on the data-driven methods used for anomaly detection based on AIS data.

Original language	English
Title of host publication	Smart Transportation Systems 2019
Editors	Lakhmi C. Jain, Xiaobo Qu, Robert J. Howlett, Lu Zhen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	29-37
Number of pages	9
ISBN (Print)	9789811386824
DOIs	https://doi.org/10.1007/978-981-13-8683-1_4
Publication status	Published - 2019
Externally published	Yes
Event	2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019 - St. Julians, Malta Duration: Jun 17 2019 → Jun 19 2019

Publication series

Name	Smart Innovation, Systems and Technologies
Volume	149
ISSN (Print)	2190-3018
ISSN (Electronic)	2190-3026

Conference

Conference	2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019
Country/Territory	Malta
City	St. Julians
Period	6/17/19 → 6/19/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.

ASJC Scopus Subject Areas

General Decision Sciences
General Computer Science

Access to Document

10.1007/978-981-13-8683-1_4

Cite this

@inproceedings{e1b46d78eccb40baa6dd102141ff917b,

title = "Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data",

abstract = "Maritime safety and security are gaining increasing concern in recent years. There are a growing number of studies aiming at improving situational awareness in the maritime domain by identifying vessel anomaly behaviors based on the data provided by the Automatic Identification System (AIS). Two types of data-driven methods are most popular in vessel anomaly detection based on AIS data: the statistical methods and machine learning methods. To improve the detection model efficiency and accuracy, hybrid models are formed by combining different types of methods. In order to incorporate expert knowledge, interactive systems are also designed and realized. In this paper, we provide a review of the popular statistical and machine learning models, as well as the hybrid models and interactive systems based on the data-driven methods used for anomaly detection based on AIS data.",

author = "Ran Yan and Shuaian Wang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019 ; Conference date: 17-06-2019 Through 19-06-2019",

year = "2019",

doi = "10.1007/978-981-13-8683-1_4",

language = "English",

isbn = "9789811386824",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "29--37",

editor = "Jain, {Lakhmi C.} and Xiaobo Qu and Howlett, {Robert J.} and Lu Zhen",

booktitle = "Smart Transportation Systems 2019",

address = "Germany",

}

Yan, R & Wang, S 2019, Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data. in LC Jain, X Qu, RJ Howlett & L Zhen (eds), Smart Transportation Systems 2019. Smart Innovation, Systems and Technologies, vol. 149, Springer Science and Business Media Deutschland GmbH, pp. 29-37, 2nd KES International Symposium on Smart Transportation Systems, KES-STS 2019, St. Julians, Malta, 6/17/19. https://doi.org/10.1007/978-981-13-8683-1_4

Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data. / Yan, Ran; Wang, Shuaian.
Smart Transportation Systems 2019. ed. / Lakhmi C. Jain; Xiaobo Qu; Robert J. Howlett; Lu Zhen. Springer Science and Business Media Deutschland GmbH, 2019. p. 29-37 (Smart Innovation, Systems and Technologies; Vol. 149).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Maritime safety and security are gaining increasing concern in recent years. There are a growing number of studies aiming at improving situational awareness in the maritime domain by identifying vessel anomaly behaviors based on the data provided by the Automatic Identification System (AIS). Two types of data-driven methods are most popular in vessel anomaly detection based on AIS data: the statistical methods and machine learning methods. To improve the detection model efficiency and accuracy, hybrid models are formed by combining different types of methods. In order to incorporate expert knowledge, interactive systems are also designed and realized. In this paper, we provide a review of the popular statistical and machine learning models, as well as the hybrid models and interactive systems based on the data-driven methods used for anomaly detection based on AIS data.

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Study of Data-Driven Methods for Vessel Anomaly Detection Based on AIS Data

Abstract

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Conference

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ASJC Scopus Subject Areas

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