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Strategies of attack-defense game for wireless sensor networks considering the effect of confidence level in fuzzy environment

机译:考虑到模糊环境中置信水平效果的无线传感器网络攻击防御游戏策略

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摘要

It is a common case that Wireless Sensor Networks are attacked by malware in the real world. According to the game theory, the action of attack-defense between Wireless Sensor Network(WSN) and malware can be regarded as a game. While substantial efforts have been made to address this issue, most of these efforts have predominantly focused on the analysis of attack-defense game in the known environment. Given that the process of gaming in real world often contains a lot of fuzzy information, we extend the focus in this line by considering the fuzzy exterior environment. Specifically, we assume the WSN attack-defense Stackelberg game is in the fuzzy environment by using fuzzy variable. Then Stackelberg game theory is utilized to calculate the equilibrium solutions of the introduced max i max chance-constrained model and min i max chance-constrained model. Based on the simulation data, this study demonstrates the confidence levels and decision perspectives affect the optimal strategy of WSN and the reliability of WSN. Finally, the novel analytical method is compared with the non-fuzzy WSN attack-defense game method. The analysis shows that the novel approach is optimal in terms of predicting the behavior of malware in resisting the attack of malware.
机译:它是一个常见的情况,无线传感器网络在现实世界中的恶意软件受到攻击。根据博弈论,无线传感器网络(WSN)和恶意软件之间的攻击行为可以被视为游戏。虽然已经提出了大量努力来解决这个问题,但大多数这些努力主要集中在已知环境中对攻击防御游戏的分析。鉴于现实世界中的游戏过程往往包含大量的模糊信息,我们通过考虑模糊的外部环境来扩展这一系列的重点。具体而言,我们假设WSN攻击防御Stackelberg游戏通过模糊变量在模糊环境中。然后,利用Stackelberg游戏理论来计算引入的MAX I最大机会约束模型和MIN I MAX限制模型的均衡解。基于仿真数据,本研究表明了置信水平和决策观点影响WSN的最佳策略和WSN的可靠性。最后,将新的分析方法与非模糊WSN攻击防御游戏方法进行比较。分析表明,在预测恶意软件的行为方面,新颖的方法是最佳的。

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