# Abnormal Ship Behavior Detection Based on AIS Data
## 2 Related Work  
Most of the existing studies have reviewed the abnormal behavior of ships based on research methods from their respective fields. Classification according to the research methods can help to elucidate the relevance and continuity of the method in the field, but because the research methods are prone to repeated use, and the existing methods tend to be complex and comprehensive, it is impossible to review the progress of the research with a single method. Therefore, we chose a classification method based on the results of the study to classify the abnormal behavior of ships. According to the literature, the research on abnormal behavior detection for ships can be divided into the following four categories according to the results: only spatial position abnormalities, spatial position and thematic attribute abnormalities, specific situation abnormalities, and no specific experimental results, with only a description of the detection framework instead. [PERSON] et al. [11] compared the input trajectory with the approximate optimal path generated by the A* algorithm to obtain the ship's abnormal score for the spatial position. This method belongs to unsupervised learning, which can effectively deal with large datasets. [PERSON] et al. [12], based on the Douglas-Peucker algorithm and density-based clustering algorithm, abstracted the ship route into leg sections and turning sections and then probabilistically characterized marine traffic based on the lateral distance of the trajectory points and other attributes. [PERSON] et al. [2] used unsupervised incremental learning to cluster and visualize routes and then used the knowledge of the routes to trajectory points for route classification and anomaly detection. [PERSON] et al. [13] used the multi-kernel non-parametric estimation method to estimate the probability density of the motion patterns of ferry vessels and set the criterion of abnormality to detect a ferry with abnormal behavior. [PERSON] [14] trained the Gaussian mixture model by taking the ship point space information and the attribute information as eigenvalues and marked the outliers by calculating the probability of the new trajectory points generated by the trained model. [PERSON] et al. [8] used the kernel density estimation method to extract the ship motion pattern from the real AIS data and identify the abnormal points. [PERSON] et al. [15] used the incremental learning method based on a fuzzy neural network to forecast and evaluate the position of a ship. [PERSON] et al. [16] combined the Gaussian method and extreme value theory to identify abnormal behavior in the trajectory flow, such as deviation, mooring, and wandering. [PERSON] et al. [17] performed unsupervised learning on the trajectory flow data obtained by the sensor based on the SOM network to detect abnormal ship movements in maritime traffic. [PERSON] [6] proposed a comprehensive anomaly detection framework, MT-MAD, to mine the spatial characteristics, sequence characteristics, and behavior characteristics of the  
Figure 1: Schematic representation of the spatial offset of trajectory points. Given the motion trajectories of three moving objects and the fact that p1 is in a low-density region during motion, it can be suspected that there is a spatial offset of p1. ship's trajectory. Finally, the three features were combined with the cooperative value to determine an abnormal situation for the ship. [PERSON] et al. [18] proposed an interactive detection system based on the DBSCAN algorithm and Markov algorithm for the multi-ship encounter problem. Based on the two-dimensional Gaussian distribution, [PERSON] et al. [10] carried out probability modeling for the normal ship-running trajectory and detected the ship characteristics by establishing a model. [PERSON] et al. [19] extracted abnormal ship traffic by correlating the ship trajectory with the existing route and then scored the degree of abnormality of the point with respect to the course, bow direction, and lateral distance of the trajectory point. [PERSON] et al.