多信道无线网络中窃听与干扰攻击的对抗策略

Anti-jamming and Eavesdropping in Wireless Multi-channel Networks

为了在无线网络中同时对抗智能干扰与窃听攻击,基于斯坦伯格博弈,通过功率控制的方法,设计窃听与干扰攻击对抗策略。在该策略中,干扰者的目标是造成源节点的能量损耗与通信质量的下降,而源节点则通过功率控制来对抗干扰者的干扰。不仅严格证明了该博弈模型均衡的存在,而且提出一种分布式学习算法,使得博弈过程可以在节点间没有信息交换的情况下高效地收敛于该均衡。在此基础上,通过分析安全容量与窃听概率,证明了提高干扰信道的信道增益可以降低窃听者的窃听概率。在性能分析中,通过与其他博弈模型的分析对比,提出的策略在对抗非合谋的智能干扰与窃听攻击方面可以达到很好的抗攻击效果。

In order to defend against smart jamming and eavesdropping attacks simultaneously in wireless multi-channel networks,based on game theory and power control,an anti-jamming transmission Stackelberg game was proposed. In the proposed game,the source node plays as the leader,chooses its transmit power and defends against the attacks. The jammer chooses its own transmit power based on the behaviors of source node. Then a distributed learning algorithm for the jammer inspired by stochastic learning automata was proposed to achieve the Stackelberg equilibrium of proposed game. The result showed that the intercept probability can be decreased through increasing the gain of wiretap channel,and the proposed model performs better in defending against the physical-layer attacks by compared with the other models.