  In maritime traffic surveillance, detecting illegal activities, such as
illegal fishing or transshipment of illicit products is a crucial task of the
coastal administration. In the open sea, one has to rely on Automatic
Identification System (AIS) message transmitted by on-board transponders, which
are captured by surveillance satellites. However, insincere vessels often
intentionally shut down their AIS transponders to hide illegal activities. In
the open sea, it is very challenging to differentiate intentional AIS shutdowns
from missing reception due to protocol limitations, bad weather conditions or
restricting satellite positions. This paper presents a novel approach for the
detection of abnormal AIS missing reception based on self-supervised deep
learning techniques and transformer models. Using historical data, the trained
model predicts if a message should be received in the upcoming minute or not.
Afterwards, the model reports on detected anomalies by comparing the prediction
with what actually happens. Our method can process AIS messages in real-time,
in particular, more than 500 Millions AIS messages per month, corresponding to
the trajectories of more than 60 000 ships. The method is evaluated on 1-year
of real-world data coming from four Norwegian surveillance satellites. Using
related research results, we validated our method by rediscovering already
detected intentional AIS shutdowns.
