Authors: Mrs. N. V. S. Sowjanya, Chavvakula Lasyavalli, Sunkara Vijay Kishore, Kammakatla Shreya, Yerra Sai Rajesh, Chelli Tarun Teja
Abstract: Maritime surveillance plays a crucial role in ensuring the safety, security, and regulation of activities in open sea environments. One of the major challenges faced by maritime authorities is the detection of vessels that intentionally disable their Automatic Identification System (AIS) transponders to conceal illegal activities such as unauthorized fishing, smuggling, or unauthorized entry into restricted maritime zones. AIS messages transmitted by ships are widely used for monitoring vessel trajectories; however, missing AIS signals may occur due to multiple reasons including satellite reception limitations, weather disturbances, or intentional shutdown of AIS devices. Distinguishing between these scenarios becomes difficult when dealing with massive volumes of satellite AIS data. This study proposes an intelligent deep learning framework for detecting intentional AIS shutdown events using self-supervised learning techniques. The proposed approach processes large-scale AIS datasets collected from satellite-based maritime surveillance systems and extracts trajectory-based features such as vessel position, speed, time intervals between messages, and movement patterns. A transformer-based deep learning architecture is used to analyse sequential AIS message data and predict whether a new AIS message is expected within a specific time window. By comparing the predicted results with the actual observations, the system identifies abnormal missing AIS receptions that may indicate intentional signal shutdown. The self-supervised learning approach allows the model to generate pseudo-labels from unlabelled AIS data, eliminating the need for manually labelled datasets. Experimental analysis demonstrates that the proposed framework can process millions of AIS messages in near real-time while achieving high prediction accuracy in detecting abnormal vessel behaviour. The integration of deep learning techniques improves the reliability and scalability of maritime surveillance systems, enabling authorities to identify suspicious vessel activities more efficiently. This framework contributes to enhancing maritime security, improving monitoring capabilities in open sea environments, and supporting timely detection of illegal maritime operations
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