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Topology Preserving Mapping for Maritime Anomaly Detection

  • Ying Wu
  • Anthony Patterson
  • Rafael D. C. Santos
  • Nandamudi L. Vijaykumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8584)

Abstract

In this paper, we present the topology preserving mapping for maritime anomaly detection. Specifically, the topology preserving mapping is applied as an unsupervised learning method, which captures the vessel behaviors and visualizes the extracted underlying data structure. At the same time, the topology preserving mapping is used as the probability estimator, where the data likelihood can be evaluated and the anomalies can be detected. Real satellite AIS data, used by the Next Generation Recognized Maritime Picture project (NG-RMP) funded by the European Space Agency, is used in this paper as the main data source. We demonstrate that the topology preserving mapping can classify the vessel observations and detect the anomalies reasonably and with high accuracy.

Keywords

Gaussian Mixture Model Anomaly Detection Normal Situation Bayesian Belief Network Normal Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arouh, S.L., Webb, M.L., Kraiman, J.B.: Automated anomaly detection processor. In: Proceedings of SPIE: Enabling Technologies, for Simulation Science (2002)Google Scholar
  2. 2.
    Bernard, J., Tekusova, T., Kohlhammer, J., Schreck, T.: Visual cluster analysis of trajectory data with interactive kohonen maps. Information Visualization 8 (2009)Google Scholar
  3. 3.
    Bishop, C.M., Svensen, M., Williams, C.K.I.: Gtm: The generative topographic mapping. Neural Computation 10, 215–234 (1998)CrossRefGoogle Scholar
  4. 4.
    Das, S., Grey, R., Gonsalves, P.: Situation assessment via bayesian belief networks. In: Proceedings of the 5th International Conference on Information Fusion, Maryland (July 2002)Google Scholar
  5. 5.
    de Boer, P.-T., Kroese, D.P., Mannor, S., Rubenstein, R.Y.: A tutorial on the cross-entropy method. Annals of Operations Research 134(1), 19–67 (2004)CrossRefGoogle Scholar
  6. 6.
    Falkman, G., Sviestins, E., Laxhammar, R.: Anomaly detection in sea traffic - a comparison of the gaussian mixture model and the kernel density estimator. In: The 12th International Conference on Information Fusion, Seattle, WA, USA (2009)Google Scholar
  7. 7.
    Fyfe, C.: Two topographic maps for data visualization. Data Mining and Kownledge Discovery 14, 207–224 (2007)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Technical Report 2000-004, Gatsby Computational Neuroscience Unit, University College, London (2000)Google Scholar
  9. 9.
    Kowalska, K., Peel, L.: Maritime anomaly detection using gaussian process active learning. In: The 15th International Conference on Information Fusion (2012)Google Scholar
  10. 10.
    Kohonen, T.: Self-organising maps. Springer (1995)Google Scholar
  11. 11.
    La Scala, B., Morelande, M., Gordon, N., Ristic, B.: Statistical analysis of motion patterns in ais data: Anomaly detection and motion prediction. In: The 11th International Conference on Information Fusion (2008)Google Scholar
  12. 12.
    Laxhammar, R.: Anomaly detection for sea surveillance. In: The 11th International Conference on Information Fusion, Cologne, German (2008)Google Scholar
  13. 13.
    Manohara, P., Rajpurohit, V.: Using self organizing networks for moving object trajectory prediction. International Journal on Artificial Intelligence and Machine Learning 9 (2009)Google Scholar
  14. 14.
    Pena, M., Barbakh, W., Fyfe, C.: Topology-Preserving Mappings for Data Visualisation. In: Principal Manifolds for Data Visualization and Dimension Reduction, pp. 131–150. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Rhodes, B.J., Bomberger, N.A., Zandipour, M., Garagic, D.: Adaptive mixture- based neural network approach for higher-level fusion and automated behavior monitoring. In: International Conference on Communications (2009)Google Scholar
  16. 16.
    Roy, J.: Anomaly detection in the maritime domain. In: Proceedings of the SPIE (2008)Google Scholar
  17. 17.
    Roy, J.: Rule-based expert system for maritime anomaly detection. In: Proceedings of the SPIE (2010)Google Scholar
  18. 18.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)Google Scholar
  19. 19.
    Wu, Y., Doyle, T.K., Fyfe, C.: Multi-layer topology preserving mapping for K-means clustering. In: Yin, H., Wang, W., Rayward-Smith, V. (eds.) IDEAL 2011. LNCS, vol. 6936, pp. 84–91. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Wu, Y., Fyfe, C.: The on-line cross entropy method for unsupervised data exploration. WSEAS Transactions on Mathematics 6(12), 865–877 (2007)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ying Wu
    • 1
  • Anthony Patterson
    • 1
  • Rafael D. C. Santos
    • 2
  • Nandamudi L. Vijaykumar
    • 2
  1. 1.Coastal and Marine Research Centre, ERIUniversity College Cork Glucksman Marine FacilityNaval BaseIreland
  2. 2.Brazilian National Institute for Space ResearchSão José dos Campos, São PauloBrazil

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