With the increasing demands for safety, efficiency, and sustainability in global shipping, Automatic Identification System (AIS) data plays an increasingly important role in maritime monitoring. AIS data contains spatial-temporal variation patterns of vessels that hold significant research value in the marine domain. However, due to its massive scale, the full potential of AIS data has long remained untapped. With its powerful sequence modeling capabilities—particularly its ability to capture long-range dependencies and complex temporal dynamics—the Transformer model has emerged as an effective tool for processing AIS data. Therefore, this paper reviews the research on Transformer-based AIS data-driven maritime monitoring, providing a comprehensive overview of the current applications of Transformer models in the marine field. The focus is on Transformer-based trajectory prediction methods, behavior detection, and prediction techniques. Additionally, this paper collects and organizes publicly available AIS datasets from the reviewed papers, performing data filtering, cleaning, and statistical analysis. The statistical results reveal the operational characteristics of different vessel types, providing data support for further research on maritime monitoring tasks. Finally, we offer valuable suggestions for future research, identifying two promising research directions. Datasets are available at https://github.com/eyesofworld/Maritime-Monitoring.

In the (preprocessing) Decisional Diffie-Hellman (DDH) problem, we are given a cyclic group G with a generator g and a prime order N, and want to prepare some advice of S, such that we can efficiently distinguish (gx,gy,gxy) from (gx,gy,gz) in time T for uniformly and independently chosen x,y,z from [N]. This is a central cryptographic problem whose computational hardness underpins many widely deployed schemes such as the Diffie–Hellman key exchange protocol.