# Abnormal Ship Behavior Detection Based on AIS Data
## 2 Related Work
[20] represented the ship's behavior using the charge value and built a model to detect abnormal behavior of a ship by defining the relationship between the ship's movement mode and the change, accumulation, and distribution of the charge value. The method proposed by [PERSON] et al. [21] is similar to the charge theory proposed by [PERSON] et al. [20], which uses the principle of a bionic animal pheromone to detect the standard path of the ship and then uses it to detect the nonstandard movement of marine traffic. Since it is difficult to consider all the aspects in anomaly detection, it is necessary to consider an anomaly in the context of the situation after it is detected [22]. For example, the deviation of the ship may be a simple response to a particular scenario such as an iceberg or hurricane [22]. Additionally, in some cases, seemingly normal and benign behavior, after combination with the information of the scene, may turn out to be deceptive or concealed abnormal behavior. [PERSON] et al. [23] built a model based on a Gaussian mixture model and self-organizing neural network and used the ship attribute information and related environmental information as input data for abnormal behavior detection. [PERSON] et al. [24] used an interactive data mining module including SOM, GMM, and Bayesian theory to build the model and judge the abnormal behavior of a ship. This method can obtain situational knowledge and artificial feedback online as well as use visual means to continuously improve the model update. [PERSON] et al. [25] learned the data by generating static and dynamic Bayesian networks and added meteorological factors and ship interaction information to enhance the learning ability of the model. Finally, the average scores of 12 abnormalities and the difference between the score and normal behavior were obtained. [PERSON] et al. [26] combined \"context information\", including marine environmental factors such as the wind direction and speed, to discriminate abnormal results based on the density clustering algorithm and finally interpreted an abnormal situation through the prior knowledge of experts. The study of [PERSON] et al. [27] focused on the detection of pirate threat behavior, combining environmental factors and ship attribute characteristics to create a ship movement model based on game theory, the Nash equilibrium principle, and reinforcement learning for the ship route and attack simulation evaluation. [PERSON] et al. [22] expounded on and discussed methods of marine anomaly detection based on four aspects--marine traffic data, methods, systems, and users--and put forward strong insights for future development. [PERSON] et al. [28] evaluated two commonly used methods of maritime anomaly detection: the Gaussian mixture model and adaptive kernel density estimator. They stated that both methods have certain limitations. Therefore, a clustering method combining scenario information and ship motion attribute features was proposed at the end of the article, but this was not verified by experiments. [PERSON] et al. [29] created a multi-layer interactive platform called Blue Hub and proposed several spatiotemporal mining techniques to enhance situational awareness. The authors opined that there is still much work to be conducted in the consolidation of past studies. [PERSON] et al. [30] used a rule-based fuzzy expert knowledge graph to monitor the port safety system. The system combined the characteristics of ship attributes, personnel, and cargo risk factors to detect and identify the behavior of a ship, but this was not verified by experiments.