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.

[1]  Rikard Laxhammar,et al.  Conformal prediction for distribution-independent anomaly detection in streaming vessel data , 2010, StreamKDD '10.

[2]  Kevin B. Korb,et al.  Learning Abnormal Vessel Behaviour from AIS Data with Bayesian Networks at Two Time Scales , 2010 .

[3]  Richard O. Lane,et al.  Maritime anomaly detection and threat assessment , 2010, 2010 13th International Conference on Information Fusion.

[4]  Göran Falkman,et al.  Anomaly detection in sea traffic - A comparison of the Gaussian Mixture Model and the Kernel Density Estimator , 2009, 2009 12th International Conference on Information Fusion.

[5]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[6]  Rikard Laxhammar,et al.  Anomaly detection for sea surveillance , 2008, 2008 11th International Conference on Information Fusion.

[7]  Douglas M. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.