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/rolepermission. 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.

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

“…The logic behind this dual mutation phase was to improvise the exploration capability of the genetic algorithm. 7 The significant feature points could be originated from imbalanced data, such it requires choosing out original data subset and subjects for the dataset resampling process. This resampling process facilitates evening-out features, equally in a balanced class, and it yields better performance for classifying outcomes.…”

“…Hence, intrusion detection design is modeled on machine-learning and big-data technology as well. 7 The technique employs pre-processing of packets, merging data, and malicious data labeling. To sort out the effective detection rate, integration of feature-selection algorithm and supervised-learning technique is carried out.…”

“…Another threat Ransomware would be unable to determine by utilizing the signature basis detection approach. Hence, intrusion detection design is modeled on machine‐learning and big‐data technology as well 7 . The technique employs pre‐processing of packets, merging data, and malicious data labeling.…”

“…To maximize the efficiency of an algorithm, in handling the issue of convergence, and performance rate, the performance of the genetic algorithm could be enhanced with a new variant, involving a new mutation operation in each iteration within the algorithm. The logic behind this dual mutation phase was to improvise the exploration capability of the genetic algorithm 7 . The significant feature points could be originated from imbalanced data, such it requires choosing out original data subset and subjects for the dataset resampling process.…”

Nussbaum gain adaptive control for full‐state constrained cyber‐physical system under network attacks and actuator failures

“…The introduction of information technology into CPSs increases the risk of system intrusion and may cause serious loss of life and property. Therefore, the safety and security of CPS is crucial 7,8 …”