关键词: 雾计算, 强化学习, 伪装攻击, 物理层安全, 攻击检测

Abstract:

Fog computing is a technology that provides distributed computing, storage and other services between cloud data centers and Internet of Things（IoT） devices. It can utilize network edges to authenticate and provide ways to interact with the clouds. In fog computing, traditional security technology is not perfect enough to realize the security between users and fog nodes. It still faces security threats such as eavesdropping attack and impersonation attack, which poses new challenges to detection technology. To solve this problem, an impersonation attack detection scheme based on DQL（Double Q-learning） algorithm for fog computing is proposed. With the help of channel parameters in the physical layer security technology, at first this proposed scheme processes the overestimation of Q value on the basis of Q-learning algorithm to obtain the optimal impersonation attack test threshold. Then the detection of impersonation attack between users and fog nodes is realized by this threshold. Finally, experimental results show that this algorithm is better than the traditional Q-learning algorithm in detecting impersonation attack, and has advantages in the security protection of fog computing.

Key words: fog computing, reinforcement learning, impersonation attack, physical layer security, attack detection