Abnormal Behaviour Detection by Using Machine Learning-Based Approaches in the Marine Environment: A Literature Survey

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.