Mobile fog computing security: A user-oriented smart attack defense strategy based on DQL

Abstract

Each fog node interacts with data from multiple end-users in mobile fog computing (MFC) networks. Malicious users can use a variety of programmable wireless devices to launch different modes of smart attacks such as impersonation attack, jamming attack, and eavesdropping attack between fog servers and legitimate users. The existing research in MFC lacks in the contributions of defense of smart attack and also requires in the discussions of subjective decision making by participants. Therefore, we propose a smart attack defense scheme for authorized users in MFC in this paper. First, we construct a static zero-sum game model between smart attackers and legitimate users based on prospect theory. Second, the double Q-learning (DQL) is proposed to restrain the attack motive of smart attackers in the dynamic environment. The proposed DQL method generates the optimum defense choice of legitimate users against smart attacks so that they can efficiently determine whether to use only physical layer security (PLS) to avoid those smart attacks. We use our scheme to contrast with the basic schemes, i.e., Q-learning scheme, the Sarsa scheme, and the greedy strategy. Experiment results prove that the proposed scheme can enhance the utility of legitimate users, restrain the attack motive of smart attackers, and further provide better security protection in the MFC environment.