Abnormal behavior detection is currently receiving much attention because of the availability of marine equipment and data allowing maritime agents to track vessels. One of the most popular tools for developing an efficient anomaly detection system is the Automatic Identification System (AIS). The aim of this paper is to explore the performance of existing well-known clustering methods for detecting the two most dangerous abnormal behaviors based on the AIS. The methods include K-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Affinity Propagation (AP), and the Gaussian Mixtures Model (GMM). In order to evaluate the performance of the clustering methods, we also used the AIS data of vessels, which were collected through the Finnish transport agency from the whole Baltic Sea for three months. Although most existing studies focus on ocean route recognition, deviations from regulated ocean routes, or irregular speed, we focused on dark ships or those sets of vessels that turn off the AIS to perform illegal activities and spiral vessel movements. The experimental results demonstrate that the K-means clustering method can effectively detect dark ships and spiral vessel movements, which are the most threatening events for maritime safety.

At this critical juncture in the world, maritime traffic and naval monitoring have become one of the hottest topics among governments to keep the marine environment safe for exporting and importing. As the amount of data used for maritime navigation, communication, and supervision has been growing, researchers are making attempts to find and develop novel, precise, and automated systems to detect anomaly behaviours of vessels in seas and ports. However, recognizing anomaly behaviours in a maritime environment is a difficult task since the wide variety of data. In this paper, we analyse and review existing machine learning-based techniques which can be utilised to recognize abnormal, and illegal ship activities. To identifying the methods and conducted this literature survey, 45 articles from peer-reviewed and high-regarded conferences have been chosen. The found papers are categorized into two groups (a) methods and (b) data. We also review and note research challenges, advantages and disadvantages of each techniques separately to motivate researchers to propose more advanced framework and tools as they are essential to consider during their research and developing stage.