Anomaly detection in spatiotemporal data in the maritime domain

Maritime security is critical for many nations to address the vulnerability of their sea lanes, ports and harbours to a variety of threats and illegal activities. With increasing volume of spatiotemporal data, it is ever more problematic to analyze the enormous volume of data in real time. This paper explores a novel approach to representing spatiotemporal data for model-driven methods for detecting patterns of anomalous behaviour in spatiotemporal datasets.