  A methodology based on deep recurrent models for maritime surveillance, over
publicly available Automatic Identification System (AIS) data, is presented in
this paper. The setup employs a deep Recurrent Neural Network (RNN)-based
model, for encoding and reconstructing the observed ships' motion patterns. Our
approach is based on a thresholding mechanism, over the calculated errors
between observed and reconstructed motion patterns of maritime vessels.
Specifically, a deep-learning framework, i.e. an encoder-decoder architecture,
is trained using the observed motion patterns, enabling the models to learn and
predict the expected trajectory, which will be compared to the effective ones.
Our models, particularly the bidirectional GRU with recurrent dropouts,
showcased superior performance in capturing the temporal dynamics of maritime
data, illustrating the potential of deep learning to enhance maritime
surveillance capabilities. Our work lays a solid foundation for future research
in this domain, highlighting a path toward improved maritime safety through the
innovative application of technology.
