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Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm

  • Luis Martí
  • Arsene Fansi-Tchango
  • Laurent Navarro
  • Marc Schoenauer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

Abstract

This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl). VorEAl partitions input space in abnormal/normal subsets using Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired approach in order to conjointly optimize classification metrics while also being able to represent areas of the data space that are not present in the training dataset. As part of the paper VorEAl is experimentally validated and contrasted with similar approaches.

Keywords

Voronoi Diagram Anomaly Detection Intrusion Detection System Voronoi Cell Artificial Immune System 
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.

Notes

Acknowledgements

This work has been funded by the project PIA-FSN-P3344-146479. Authors wish to thank the reviewers for their fruitful comments.

References

  1. 1.
    Bader, J.: Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods. Ph.D. thesis, ETH Zurich, Switzerland (2010)Google Scholar
  2. 2.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), Article No. 15 (2009)Google Scholar
  3. 3.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  4. 4.
    García, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)zbMATHGoogle Scholar
  5. 5.
    Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., Sebag, M.: Compact unstructured representations for evolutionary design. Appl. Intell. 16, 139–155 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Ji, Z., Dasgupta, D.: Real-valued negative selection algorithm with variable-sized detectors. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Kim, J., Bentley, P.J., Aickelin, U., Greensmith, J., Tedesco, G., Twycross, J.P.: Immune system approaches to intrusion detection a review. Nat. Comput. 6(4), 413–466 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Martí, L., Fansi-Tchango, A., Navarro, L., Schoenauer, M.: VorAIS: a multi-objective Voronoi diagram-based artificial immune system. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (GECCO 2009), ACM (2016)Google Scholar
  9. 9.
    Northcutt, S., Novak, J.: Network Intrusion Detection. Sams Publishing, Indianapolis (2002)Google Scholar
  10. 10.
    Schoenauer, M.: Shape representation for evolutionary optimization and identification in structural mechanics. In: Winter, G., Périaux, J., Galán, M., Cuesta, P. (eds.) Genetic Algorithms in Engineering and Computer Science (EUROGEN 1995), pp. 443–464 (1995)Google Scholar
  11. 11.
    Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, New York (1981). (1995–2nd edn.)zbMATHGoogle Scholar
  12. 12.
    Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Luis Martí
    • 1
    • 2
  • Arsene Fansi-Tchango
    • 3
  • Laurent Navarro
    • 3
  • Marc Schoenauer
    • 1
  1. 1.TAO Team, CNRS/INRIA/LRI, Université Paris-SaclayParisFrance
  2. 2.Universidade Federal FlumnenseNiteróiBrazil
  3. 3.Thalés ResearchParisFrance

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