Competitive coevolutionary algorithm decision support

Sanchez, Daniel Prado; Pertierra, Marcos; Hemberg, Erik; O’Reilly, Una-May

doi:10.1145/3205651.3205784

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…In fact, competitive coevolution poses general challenges when used for design optimization. The following ones in RIVALS make it difficult to present a designer with clear information derived solely from multiple simulation runs [115,111]:…”

Section: Elements Of Rivalsmentioning

confidence: 99%

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Adversarial genetic programming for cyber security: a rising application domain where GP matters

O’Reilly¹,

Toutouh²,

Pertierra³

et al. 2020

Genet Program Evolvable Mach

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Cyber security adversaries and engagements are ubiquitous and ceaseless. We delineate Adversarial Genetic Programming for Cyber Security, a research topic that, by means of genetic programming (GP), replicates and studies the behavior of cyber adversaries and the dynamics of their engagements. Adversarial Genetic Programming for Cyber Security encompasses extant and immediate research efforts in a vital problem domain, arguably occupying a position at the frontier where GP matters. Additionally, it prompts research questions around evolving complex behavior by expressing different abstractions with GP and opportunities to reconnect to the Machine Learning, Artificial Life, Agent-Based Modeling and Cyber Security communities. We present a framework called RIVALS which supports the study of network security arms races. Its goal is to elucidate the dynamics of cyber networks under attack by computationally modeling and simulating them.

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Section: Elements Of Rivalsmentioning

confidence: 99%

“…To this end, RIVALS provides an additional decision support component, named ESTABLO [111,115], see Fig. 9.…”

Section: Elements Of Rivalsmentioning

confidence: 99%

See 1 more Smart Citation

Adversarial genetic programming for cyber security: a rising application domain where GP matters

O’Reilly¹,

Toutouh²,

Pertierra³

et al. 2020

Genet Program Evolvable Mach

Self Cite

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“…One method looks at average and final best fitness scores averaged across all runs. The other method creates a compendium [24] by calculating MEU, MinMax, and inverse Pareto front ratio scores to create an objective combined score for a single best solution.…”

Section: Comparisonmentioning

confidence: 99%

Investigating algorithms for finding nash equilibria in cyber security problems

Zhang¹,

Hemberg²

2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Distributed Denial of Service (DDoS) cyber attacks continue to increase and cause disruptions in both industry and politics. As more critical information and services are provided through networks, it is important to keep these networks available. However, since adversaries are continuously changing and adapting, stationary defense strategies do not effectively secure networks against attacks. We investigate Nash equilibria in cyber security problems by modeling attacker-defender interactions using competitive coevolutionary algorithms. In particular, we examined the performances of two algorithms (and their variations) that look for Nash equilibria, NashSolve and HybridCoev, and compared their performances against other existing heuristics. Using two evaluation techniques, one that looked at average fitness scores and one that created a compendium of MEU, MinMax, and inverse Pareto front ratio scores, we found that NashSolve and HybridCoev did not perform significantly better for both attacker and defender populations relative to other heuristics.

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