| Title | An Edge-Cloud Synergy Integrated Security Decision-Making Method for Industrial Cyber-Physical Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Xing, Hang, Zhou, Chunjie, Ye, Xinhao, Zhu, Meipan |
| Conference Name | 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) |
| Date Published | nov |
| Keywords | Bayes methods, cloud computing, composability, compositionality, Computational Intelligence, Computational modeling, cryptography, decision-making, edge-cloud synergy, game theory, Games, Image edge detection, industrial cyber-physical system (ICPS), pubcrawl, risk management, security |
| Abstract | With the introduction of new technologies such as cloud computing and big data, the security issues of industrial cyber-physical systems (ICPSs) have become more complicated. Meanwhile, a lot of current security research lacks adaptation to industrial system upgrades. In this paper, an edge-cloud synergy framework for security decision-making is proposed, which takes advantage of the huge convenience and advantages brought by cloud computing and edge computing, and can make security decisions on a global perspective. Under this framework, a combination of Bayesian network-based risk assessment and stochastic game model-based security decision-making is proposed to generate an optimal defense strategy to minimize system losses. This method trains models in the clouds and infers at the edge computing nodes to achieve rapid defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the feasibility of the approach. |
| DOI | 10.1109/DDCLS49620.2020.9275040 |
| Citation Key | xing_edge-cloud_2020 |
An Edge-Cloud Synergy Integrated Security Decision-Making Method for Industrial Cyber-Physical Systems