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A Non-Zero-Sum Game Model for Optimal Cyber Defense Strategies

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arxiv 2505.16049 v1 pith:CZ34LDKQ submitted 2025-05-21 cs.GT

A Non-Zero-Sum Game Model for Optimal Cyber Defense Strategies

classification cs.GT
keywords cybermodelnetworkattackerscostscybersecuritydefenseexploits
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the contemporary digital landscape, cybersecurity has become a critical issue due to the increasing frequency and sophistication of cyber attacks. This study utilizes a non-zero-sum game theoretical framework to model the strategic interactions between cyber attackers and defenders, with the objective of identifying optimal strategies for both. By defining precise payoff functions that incorporate the probabilities and costs associated with various exploits, as well as the values of network nodes and the costs of deploying honeypots, we derive Nash equilibria that inform strategic decisions. The proposed model is validated through extensive simulations, demonstrating its effectiveness in enhancing network security. Our results indicate that high-probability, low-cost exploits like Phishing and Social Engineering are more likely to be used by attackers, necessitating prioritized defense mechanisms. Our findings also show that increasing the number of network nodes dilutes the attacker's efforts, thereby improving the defender's payoff. This study provides valuable insights into optimizing resource allocation for cybersecurity and highlights the scalability and practical applicability of the game-theoretic approach.

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Cited by 2 Pith papers

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  1. Strategic commitments shape collective cybersecurity under AI inequality

    cs.AI 2026-05 unverdicted novelty 5.0

    Subsidized commitment by a small group of defenders in an evolutionary game model significantly increases strong defense adoption, suppresses attacks, and improves system resilience under AI access inequality.

  2. Strategic commitments shape collective cybersecurity under AI inequality

    cs.AI 2026-05 unverdicted novelty 4.0

    Targeted subsidies for committed defenders in an evolutionary game model of AI-unequal cybersecurity significantly increase strong defense adoption, suppress attacks, and enhance overall resilience.