Intelligent attack defense scheme based on DQL algorithm in mobile fog computing

Abstract

Fog computing is a technology that can expands the network computing mode of cloud computing and extends network computing from the network center to the network edge. It adds fog layer between cloud data center layer and Internet of Things (IoT) device layer, and provides data storage, processing, forwarding and other functions for devices using the network edge. In mobile fog computing (MFC) networks, fog nodes communicate with end users through wireless networks. Malicious users can choose different attack modes to attack legitimate users. There is a lack of research on the subjective choice of attack modes for malicious users in current work. To solve this problem, an intelligent attack defense scheme based on Double Q-learning (DQL) algorithm in MFC is proposed. Firstly, the security model involving malicious users in MFC is described. Based on Prospect Theory (PT), a static method of subjective zero-sum game between malicious users and legitimate users is constructed. Secondly, a dynamic subjective game scheme based on DQL algorithm is proposed to resist intelligent attacks. The simulation results show that compared with the Q-learning-based method for resisting intelligent attacks, the proposed method can enhance the security of MFC network and enhance the protection performance.