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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bader, J.: Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods. Ph.D. thesis, ETH Zurich, Switzerland (2010)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), Article No. 15 (2009)
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)
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)
Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., Sebag, M.: Compact unstructured representations for evolutionary design. Appl. Intell. 16, 139–155 (2002)
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)
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)
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)
Northcutt, S., Novak, J.: Network Intrusion Detection. Sams Publishing, Indianapolis (2002)
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)
Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, New York (1981). (1995–2nd edn.)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)
Acknowledgements
This work has been funded by the project PIA-FSN-P3344-146479. Authors wish to thank the reviewers for their fruitful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Martí, L., Fansi-Tchango, A., Navarro, L., Schoenauer, M. (2016). Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_65
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)