Research On Performance Property Measurement Of Trusted Industrial Control Network System

Industrial control network system is the key to realize the automation of industrial manufacturing,and it is an essential indicators to measure the national industry manufacturing level.With the development of Internet of things,big data and artificial intelligent technology,its safe operation has become an important part of national security strategy.Trusted computing has become an vital branch of the international information security academic field,attracting the attention and research from more and more scholars around the world.The main work and contributions of this thesis are as follows:(1)Firstly,the security status and security requirements of industrial control network system are analyzed,the realization technology of trusted industrial control network system is studied,and a trusted Industrial control network system architecture is proposed.The trusted attributes of the trusted Industrial control network system are security,survivability and controllability.(2)According to the characteristics of the industrial control network system,the security is subdivided for its availability,reliability and the number of failures per unit time.A multi-state reward-Markov security measurement method is proposed,which quantitatively measures its availability,reliability and number of failures per unit of time.(3)A quantitative measure model of continuous time Markov survivability of industrial control network system is established.The measurement model is divided into two kinds,there are static and dynamic.The general generation function and analytic hierarchy process are introduced to solve the problem of "state explosion" of the model,which reduces the computational complexity.In order to solve the no strict Markov properties in some industrial situations,a continuous time multi-state semi-Markov survivability measurement method is explored.(4)According to the instantaneous performance of industrial control network system and its average output performance defect value,a controllability discriminant method based on output performance is proposed.In order to improve the controllability of industrial control network system and to identify its key parts,a controllability measurement method based on complex network is proposed.In order to solve the specific action set of controllability optimization measures,a controllability measurement method based on Markov decision process is proposed.In order to solve the problem that the relevant parameters of Markov decision controllability measure are uncertain in some situations,a controllability measure method based on reinforcement learning is proposed.Aiming at the problem of controllability optimization,a optimization model based on Markov decision process and a optimization model based on reinforcement learn ing are proposed.The research results of the thesis show that it have laid a solid theoretical foundation for the construction of trusted industrial control network system and provided an effective way to realize it.