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

Abstract

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.