  The automatic identification system (AIS) reports vessels' static and dynamic
information, which are essential for maritime traffic situation awareness.
However, AIS transponders can be switched off to hide suspicious activities,
such as illegal fishing, or piracy. Therefore, this paper uses real world AIS
data to analyze the possibility of successful detection of various anomalies in
the maritime domain. We propose a multi-class artificial neural network
(ANN)-based anomaly detection framework to classify intentional and
non-intentional AIS on-off switching anomalies. The multi-class anomaly
framework captures AIS message dropouts due to various reasons, e.g., channel
effects or intentional one for carrying illegal activities. We extract
position, speed, course and timing information from real world AIS data, and
use them to train a 2-class (normal and anomaly) and a 3-class (normal, power
outage and anomaly) anomaly detection models. Our results show that the models
achieve around 99.9% overall accuracy, and are able to classify a test sample
in the order of microseconds.
