Detection of vessel anomalies - a Bayesian network approach

In this paper we describe a data mining approach for detection of anomalous vessel behaviour. The suggested approach is based on Bayesian networks which have two important advantages compared to opaque machine learning techniques such as neural networks: (1) possibility to easily include expert knowledge into the model, and (2) possibility for humans to understand and interpret the learned model. Our approach is implemented and tested on synthetic data, where initial results show that it can be used for detection of single-object anomalies such as speeding.

[1]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[2]  Lars Niklasson,et al.  Trajectory clustering for coastal surveillance , 2007, 2007 10th International Conference on Information Fusion.

[3]  Franz von Kutschera,et al.  Causation , 1993, J. Philos. Log..

[4]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[5]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[6]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[7]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[8]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[9]  James B. Kraiman,et al.  Automated anomaly detection processor , 2002, SPIE Defense + Commercial Sensing.

[10]  Judea Pearl,et al.  An Algorithm for Deciding if a Set of Observed Independencies Has a Causal Explanation , 1992, UAI.

[11]  Leonid Portnoy,et al.  Intrusion detection with unlabeled data using clustering , 2000 .

[12]  Dr Håkan Warston,et al.  Ground surveillance and fusion of ground target sensor data in a Network Based Defense , 2004 .

[13]  Linda C. van der Gaag,et al.  Properties of Sensitivity Analysis of Bayesian Belief Networks , 2002, Annals of Mathematics and Artificial Intelligence.

[14]  Adnan Darwiche,et al.  Inference in belief networks: A procedural guide , 1996, Int. J. Approx. Reason..

[15]  P. Spirtes,et al.  An Algorithm for Fast Recovery of Sparse Causal Graphs , 1991 .

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  David Heckerman,et al.  Bayesian Networks for Data Mining , 2004, Data Mining and Knowledge Discovery.