Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm

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


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.


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.



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


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Luis Martí
    • 1
    • 2
    Email author
  • 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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