Abnormal Ship Behavior Detection Based on AIS Data

With the development of navigation globalization and ship dehumanization, the contradiction between the increasing demand for ship behavior supervision and limited traffic service resources is obvious, and the frequent occurrence of accidents at sea is a problem. The monitoring of abnormal ship behavior is an important link in maritime transportation. With the popularization of the automatic identification system and increasing research in the maritime field, the AIS is widely used in the management of ship static information and the real-time sharing of dynamic information. The generated moving ship trajectory data provide a new opportunity for research into abnormal ship behavior and its detection. In light of the current situation of abnormal ship behavior research, we detected abnormal ship behavior from the point of view of spatial information and thematic information based on moving ship trajectory data. Therefore, this study first modeled the cognition of abnormal ship behavior. Then, based on the cognition of group ship behavior rules, we used a method based on graph structure learning to mine maritime routes from the perspective of ship spatial information. Next, we used Rayda's criterion to detect the anomalous behavior of ships in space. Then, based on the isolation forest algorithm, we detected and described the abnormal behavior shown by ship thematic information. The experimental results show that the framework proposed in this paper can effectively detect the abnormal behavior of ships.