Anomaly detection in vessel tracks using Bayesian networks

In recent years electronic tracking has provided voluminous data on vessel movements, leading researchers to try various data mining techniques to find patterns and, especially, deviations from patterns, i.e., for anomaly detection. Here we describe anomaly detection with data mined Bayesian Networks, learning them from real world Automated Identification System (AIS) data, and from supplementary data, producing both dynamic and static Bayesian network models. We find that the learned networks are quite easy to examine and verify despite incorporating a large number of variables. We also demonstrate that combining dynamic and static modelling approaches improves the coverage of the overall model and thereby anomaly detection performance.

• We learn BN normality models from AIS vessel data for anomaly detection.
• We generate models at two levels of abstraction: time series and track summary.
• We give a knowledge engineering BN process for detecting general anomalous behaviour.
• The learned models are transparent and can be easily understood by domain experts.
• Combining the models improves the performance of either alone.