A Cloud-Fog-Edge Closed-Loop Feedback Security Risk Prediction Method

Abstract

In recent years, with the opening of the “smart age” curtain, smart devices dominated by technologies such as robots, drones, and intelligent perception have gradually moved to the center of the Intelligent CPSS stage. However, the new security risks of the Intelligent CPSS have also become increasingly prominent. Especially in recent years, in Ukraine and Venezuela's power attack incidents, a series of related attacks always occur simultaneously. This is a multi-task compound attack. This paper designs a set of Cloud-Fog-Edge closed-loop feedback security risk prediction strategies for multi-task compound attacks based on the offensive and defensive ideas of intelligent games, combining classified deep Boltzmann machines and Markov time-varying models. This strategy can be used for various types of power intelligent system terminals, and realizes security risk prediction with modularity, interoperability, open interfaces and compliance with open standards. Interoperability with other safety equipment can also be achieved through standardized interfaces to form system security protection capabilities to meet the actual needs of the industry Internet system. Experimental results show that the method is superior to the typical traditional method.

Keywords

• Risk Prediction
• classification deep Boltzmann machine
• Markov time-varying model