Investigating algorithms for finding Nash equilibria in cyber security problems

Abstract

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.