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Anomaly Detection of Trajectories with Kernel Density Estimation by Conformal Prediction

  • James Smith
  • Ilia Nouretdinov
  • Rachel Craddock
  • Charles Offer
  • Alexander Gammerman
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 437)

Abstract

This paper describes conformal prediction techniques for detecting anomalous trajectories in the maritime domain. The data used in experiments were obtained from Automatic Identification System (AIS) broadcasts – a system for tracking vessel locations. A dimensionality reduction package is used and a kernel density estimation function as a non-conformity measure has been applied to detect anomalies. We propose average p-value as an efficiency criteria for conformal anomaly detection. A comparison with a k-nearest neighbours non-conformity measure is presented and the results are discussed.

Keywords

Anomaly Detection Kernel Density Estimation Conformity Measure Conformal Predictor Conformal Prediction 
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.

References

  1. 1.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection A Survey. ACM Computing Surveys (CSUR) (2009), dl.acm.org
  2. 2.
    Vovk, V., Gammerman, A., Shafer, G.: Algorithmic learning in a random world. Springer (2005)Google Scholar
  3. 3.
    Gammerman, A., Vovk, V.: Hedging predictions in machine learning. The Computer Journal 50(2), 151–163Google Scholar
  4. 4.
    Laxhammar, R., Falkman, G.: Sequential Conformal Anomaly Detection in Trajectories based on Hausdorff Distance. In: 2011 Proceedings of the 14th International Conference on Information Fusion (FUSION) (2011)Google Scholar
  5. 5.
    Laxhammar, R., Falkman, G.: Conformal prediction for distribution-independent anomaly detection in streaming vessel data. In: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, pp. 47–55. ACM (2010)Google Scholar
  6. 6.
    Laxhammar, R., Falkman, G.: Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Karatzas, K., Sioutas, S. (eds.) AIAI 2012, Part II. IFIP AICT, vol. 382, pp. 192–202. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Laxhammar, R.: Conformal anomaly detection: Detecting abnormal trajectories in surveillance applications. PhD Thesis, University of Skovde (2014)Google Scholar
  8. 8.
    Lei, J., Robins, J., Wasserman, L.: Distribution-Free Prediction Sets. Journal of the American Statistical Association 108(501), 278–287 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Van der Maaten, L., Hinton, G.: Visualizing Data using t-SNE. Journal of Machine Learning Research 9(11) (2008), http://homepage.tudelft.nl/19j49/t-SNE.html

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • James Smith
    • 1
  • Ilia Nouretdinov
    • 1
  • Rachel Craddock
    • 2
  • Charles Offer
    • 2
  • Alexander Gammerman
    • 1
  1. 1.Computer Learning Research CenterRoyal Holloway University of LondonUK
  2. 2.ThalesUK

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