# Abnormal Ship Behavior Detection Based on AIS Data
## 5 Conclusions  
The marine environment is complex and variable, and the unconstrained motion of a ship's trajectory increases the uncertainty of the data. Based on the characteristics of the target, this study explored a ship's motion behavior and further detected its abnormal behavior based on the two aspects of spatial attributes and thematic attributes.  
The factors affecting a ship during the course of travel, including the range price, weather, traffic control, and geographical environment, usually follow the following factually standardized maritime lanes, so we extended the inversed graph-embedding technology based on historical AIS data to detect and characterize the structure of the maritime routes. Then, based on the extracted structure of the maritime routes, we detected the departure of the ship from the route from the spatial attributes. Next, we refined the data, grouped the ship trajectory groups according to the network structure based on the maritime routes, and detected abnormal ship thematic attributes and scored their degrees based on the isolation forest algorithm. Based on the AIS data for ship navigation between the East China Sea and the Bohai Sea, we detected and scored the abnormal ship motion with spatial attributes and thematic attributes. After detection, we were able to identify a number of abnormal points with high scores. We combined these with the actual physical environment of the trajectory points to find and explain them and then identified more meaningful points and phenomena.  
AIS data are an important tool for global sea state monitoring, but their regulation and privacy remain to be discussed. Relevant maritime authorities believe that the privacy and sovereignty of maritime vessels require the encryption of AIS data. At the same time, there is a lack of research on the feature extraction and correlation of high-dimensional datasets, such as those for the weather and the marine environment. The non-spatiotemporal information of ships and the situational information in driving usually contain other behavior patterns of ships. Therefore, considering the static information and environmental information of ships could provide new ideas for the detection of abnormal ship behaviors.  
Figure 18: The thematic attribute set is the schematic diagram of abnormal ship point detection with average characteristics. (**a**) The changes in the average speed and average acceleration of the ship. (**b**) The realistic mapping of the high anomaly score points in (**a**) in the geographic environment.  
**Author Contributions:** Conceptualization, Y.S., C.L., X.Y. and M.D.; data curation, C.L.; methodology, C.L., X.Y. and Y.S.; visualization, C.L. and X.Y.; writing--original draft, C.L. and X.Y.; writing--review and editing, C.L., X.Y. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by grants from the National Natural Science Foundation of China (41901319 and 42171459), the Key Program of the National Natural Science Foundation of China (41730105), and the Independent Exploration and Innovation Project Fund Designated for Graduate Students of Central South University (2021 zzts0819).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was carried out in part using computing resources at the High Performance Computing Platform of Central South University.  
**Conflicts of Interest:** The authors declare no conflict of interest.