Defending pollution attacks in network coding enabled wireless
            <i>ad hoc</i>
            networks: a game‐theoretic framework

Liu, Xiang; Huang, Jie; Yao, Yiyang; Qi, Chunyang; Zong, Guowen

doi:10.1049/iet-com.2019.1372

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…Wu et al [13] used the reinforcement learning algorithm to solve Nash equilibrium and realize security situation analysis and prediction of an intelligent system. Liu et al [14] investigated how to achieve such a trade-off optimally of cost-revenue by proposing a two-player strategic game model between the attack and the defender.…”

Section: Related Workmentioning

confidence: 99%

A Defense Strategy Selection Method Based on the Cyberspace Wargame Model

Zhu¹,

et al. 2021

Security and Communication Networks

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Network defenders always face the problem of how to use limited resources to make the most reasonable decision. The network attack-defense game model is an effective means to solve this problem. However, existing network attack-defense game models usually assume that defenders will no longer change defense strategies after deploying them. However, in an advanced network attack-defense confrontation, defenders usually redeploy defense strategies for different attack situations. Therefore, the existing network attack-defense game models are challenging to accurately describe the advanced network attack-defense process. To address the above challenges, this paper proposes a defense strategy selection method based on the network attack-defense wargame model. We model the advanced network attack-defense confrontation process as a turn-based wargame in which both attackers and defenders can continuously adjust their strategies in response to the attack-defense posture and use the Monte Carlo tree search method to solve the optimal defense strategy. Finally, a network example is used to illustrate the effectiveness of the model and method in selecting the optimal defense strategy.

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Section: Related Workmentioning

confidence: 99%