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Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest

文献类型：期刊论文

作者Zheng, Hailin2,3; Hu, Qinyou2; Yang, Chun2; Mei, Qiang2,4; Wang, Peng1,2; Li, Kelong3
刊名JOURNAL OF MARINE SCIENCE AND ENGINEERING
出版日期2023-08-01
卷号11期号:8页码:22
关键词automatic identification system spoofing ship missing points jumping points trajectory segmentation isolation forest
DOI10.3390/jmse11081516
英文摘要Outliers of ship trajectory from the Automatic Identification System (AIS) onboard a ship will affect the accuracy of maritime situation awareness, especially for a regular ship trajectory mixed with a spoofing ship, which has an unauthorized Maritime Mobile Service Identification code (MMSI) owned by a regular ship. As has been referred to in the literature, the trajectory of these spoofing ships would simply be removed, and more AIS data would be lost. The pre-processing of AIS data should aim to retain more information, which is more helpful in maritime situation awareness for the Maritime Safety Administration (MSA). Through trajectory feature mining, it has been found that there are obvious differences between the trajectory of a regular ship and that of a regular ship mixed with a spoofing ship, such as in terms of speed and distance between adjacent trajectory points. However, there can be a long update time interval in the results of severe missing trajectories of a ship, bringing challenges in terms of the identification of spoofing ships. In order to accurately divide the regular ship trajectory and spoofing ship trajectory, combined with trajectory segmentation by the update time interval threshold, the isolation forest was adopted in this work to train the labeled trajectory point of a regular ship mixed with a spoofing ship. The experimental results show that the average accuracy of the identification of spoofing ships using isolation forest is 88.4%, 91%, 93.1%, and 93.3%, corresponding to different trajectory segmentation by update time intervals (5 h, 10 h, 15 h, and 20 h). The research conducted in this study can almost eliminate the outliers of ship trajectory, and it also provides help for maritime situation awareness for the MSA.
资助项目Project of Ministry of Transport[2020MS6162] ; Project of Zhoushan science and Technology Bureau[2021C21010] ; National innovation and entrepreneurship training program for Zhejiang Ocean University[202210340043]
WOS研究方向Engineering ; Oceanography
语种英语
WOS记录号WOS:001057206600001
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/21393]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Hu, Qinyou
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China
2.Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China
3.Zhejiang Ocean Univ, Sch Naval Architecture & Maritime, Zhoushan 316022, Peoples R China
4.Jimei Univ, Nav Inst, Xiamen 361021, Peoples R China
推荐引用方式
GB/T 7714		 Zheng, Hailin,Hu, Qinyou,Yang, Chun,et al. Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2023,11(8):22.
APA Zheng, Hailin,Hu, Qinyou,Yang, Chun,Mei, Qiang,Wang, Peng,&Li, Kelong.(2023).Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest.JOURNAL OF MARINE SCIENCE AND ENGINEERING,11(8),22.
MLA Zheng, Hailin,et al."Identification of Spoofing Ships from Automatic Identification System Data via Trajectory Segmentation and Isolation Forest".JOURNAL OF MARINE SCIENCE AND ENGINEERING 11.8(2023):22.

入库方式： OAI收割

来源：计算技术研究所

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