What Is Your MOVE: Modeling Adversarial Network Environments

Karlo Knezevic*, Stjepan Picek, Domagoj Jakobovic, Julio Hernandez-Castro

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Finding optimal adversarial dynamics between defenders and attackers in large network systems is a complex problem one can approach from several perspectives. The results obtained are often not satisfactory since they either concentrate on only one party or run very simplified scenarios that are hard to correlate with realistic settings. To truly find which are the most robust defensive strategies, the adaptive attacker ecosystem must be given as many degrees of freedom as possible, to model real attacking scenarios accurately. We propose a coevolutionary-based simulator called MOVE that can evolve both attack and defense strategies. To test it, we investigate several different but realistic scenarios, taking into account features such as network topology and possible applications in the network. The results show that the evolved strategies far surpass randomly generated strategies. Finally, the evolved strategies can help us to reach some more general conclusions for both attacker and defender sides.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation
Subtitle of host publication23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings
EditorsPedro A. Castillo, Juan Luis Jiménez Laredo, Francisco Fernández de Vega
Place of PublicationCham
PublisherSpringer
Pages260-275
Number of pages16
ISBN (Electronic)978-3-030-43722-0
ISBN (Print)978-3-030-43721-3
DOIs
Publication statusPublished - 2020
Event23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020 - Seville, Spain
Duration: 15 Apr 202017 Apr 2020
Conference number: 23

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume12104
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020
CountrySpain
CitySeville
Period15/04/2017/04/20
OtherVirtual/online event due to COVID-19

Bibliographical note

Virtual/online event due to COVID-19

Keywords

  • Attack/defense strategies
  • Coevolutionary algorithms
  • Network security

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