UC San Diego

This research paper presents an innovative approach to anomaly detection in maritime data, focusing on the identification of anomalous vessel groups through Automatic Identification System (AIS) data. Traditional AIS anomaly detection systems are challenged by a narrow focus on individual vessel anomalies which leaves out critical collective action information. Our work seeks to overcome this limitation by introducing a graph modeling methodology that emphasizes the collective behavior of vessels, a critical aspect often overlooked in current frameworks. We work towards a system that detects unusual patterns of coordination among groups of vessels, which is particularly relevant for defense contexts where threats such as smuggling, piracy, and adversarial operations are typically carried out by networks of collaborating entities. The research contributes to the field by offering a more comprehensive understanding of maritime traffic through the lens of collective dynamics, enhancing the detection capabilities of AIS monitoring systems. The implications of our methodology are far-reaching, providing a foundational strategy for future research in anomaly detection that could be applied to various domains where group coordination plays a pivotal role in defining anomalous behavior. This paper details our graph-based approach and suggests a trajectory for subsequent investigations into the broader applications of detecting coordinated anomalies.