What Is Your MOVE: Modeling Adversarial Network Environments

Karlo Knezevic; Stjepan Picek; Domagoj Jakobovic; Julio Hernandez-Castro

doi:10.1007/978-3-030-43722-0_17

What Is Your MOVE: Modeling Adversarial Network Environments

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

^*Corresponding author for this work

Cyber Security

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-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 language	English
Title of host publication	Applications of Evolutionary Computation
Subtitle of host publication	23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings
Editors	Pedro A. Castillo, Juan Luis Jiménez Laredo, Francisco Fernández de Vega
Place of Publication	Cham
Publisher	Springer
Pages	260-275
Number of pages	16
ISBN (Electronic)	978-3-030-43722-0
ISBN (Print)	978-3-030-43721-3
DOIs	https://doi.org/10.1007/978-3-030-43722-0_17
Publication status	Published - 2020
Event	23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020 - Seville, Spain Duration: 15 Apr 2020 → 17 Apr 2020 Conference number: 23

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer
Volume	12104
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020
Country/Territory	Spain
City	Seville
Period	15/04/20 → 17/04/20
Other	Virtual/online event due to COVID-19

Bibliographical note

Virtual/online event due to COVID-19

Keywords

Attack/defense strategies
Coevolutionary algorithms
Network security

Access to Document

10.1007/978-3-030-43722-0_17

Adversarial_networks(1)Accepted author manuscript, 279 KB

Cite this

Knezevic, K., Picek, S., Jakobovic, D., & Hernandez-Castro, J. (2020). What Is Your MOVE: Modeling Adversarial Network Environments. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation : 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 260-275). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 ). Springer. https://doi.org/10.1007/978-3-030-43722-0_17

Knezevic, Karlo ; Picek, Stjepan ; Jakobovic, Domagoj et al. / What Is Your MOVE : Modeling Adversarial Network Environments. Applications of Evolutionary Computation : 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings. editor / Pedro A. Castillo ; Juan Luis Jiménez Laredo ; Francisco Fernández de Vega. Cham : Springer, 2020. pp. 260-275 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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Knezevic, K, Picek, S, Jakobovic, D & Hernandez-Castro, J 2020, What Is Your MOVE: Modeling Adversarial Network Environments. in PA Castillo, JL Jiménez Laredo & F Fernández de Vega (eds), Applications of Evolutionary Computation : 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12104 , Springer, Cham, pp. 260-275, 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020, Seville, Spain, 15/04/20. https://doi.org/10.1007/978-3-030-43722-0_17

What Is Your MOVE: Modeling Adversarial Network Environments. / Knezevic, Karlo; Picek, Stjepan; Jakobovic, Domagoj et al.
Applications of Evolutionary Computation : 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings. ed. / Pedro A. Castillo; Juan Luis Jiménez Laredo; Francisco Fernández de Vega. Cham: Springer, 2020. p. 260-275 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 ).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T2 - 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of EvoStar 2020

AU - Knezevic, Karlo

AU - Picek, Stjepan

AU - Jakobovic, Domagoj

AU - Hernandez-Castro, Julio

N1 - Conference code: 23

PY - 2020

Y1 - 2020

N2 - 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.

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SN - 978-3-030-43721-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Knezevic K, Picek S, Jakobovic D, Hernandez-Castro J. What Is Your MOVE: Modeling Adversarial Network Environments. In Castillo PA, Jiménez Laredo JL, Fernández de Vega F, editors, Applications of Evolutionary Computation : 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings. Cham: Springer. 2020. p. 260-275. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-43722-0_17

What Is Your MOVE: Modeling Adversarial Network Environments

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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