An Improved Genetic Algorithm for Safety and Availability Checking in Cyber-Physical Systems

Abstract

Cross-IoT infrastructure access frequently occurs when performing tasks in a distributed computing infrastructure of a cyber-physical system (CPS). The access control technology that ensure secure access cross-IoT infrastructure usually automatically establish relationships between user-attribute/role-permission. How to efficiently determine whether an automatic authorization access control state satisfies the safety and availability requirements of a system is a huge challenge. Existing work often focuses on a single aspect of safety or availability, while ignoring the differences between permissions and the differences between users. In this paper, we first propose a fine-grained personalization policy that takes into account the specificity of permissions/users and describes the safety, availability and efficiency requirements of an access control system in CPS. Second, we define a Personalization Policy Checking (PPC) Problem to determine whether a given personalization policy is satisfied in an access control state. We give the computational complexity of the PPC problem in different subcases, and show that it is NP-complete in general. Third, we design a binary genetic search algorithm, whose improvements mainly include continuous update and selection of the best chromosomes in the population for iteration, and exploring and determining the optimal crossover and mutation probabilities, thereby improving the convergence efficiency of the algorithm. Finally, simulation results show the effectiveness of our proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale CPS system.

Keywords

• Access control
• personalization policy
• genetic algorithm
• cyber-physical system