Industrial Security Solution for Virtual Reality

• Published in: IEEE internet of things journal Vol. 8; no. 8; pp. 6273 - 6281
• Main Authors: Lv, Zhihan, Chen, Dongliang, Lou, Ranran, Song, Houbing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Class and sample weighted C-support vector machine (CSWC-SVM) algorithm; Classification; Classification algorithms; Control stability; Control systems; Industrial control; industrial control system; Industrial electronics; Industrial safety; Intrusion detection; Kernel functions; network intrusion detection; Polynomials; Real-time systems; Simulation; Solid modeling; support vector machine; Support vector machines; Testing time; Virtual reality; Weight
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3004469

In order to protect industrial safety, improve the operation stability of the industrial control system, conduct the response measures for network environment attacked by the external world, and realize simulation in virtual reality environment, in this study, class and sample weighted C-support vector machine (CSWC-SVM) algorithm is first proposed using SVM. Then, the intrusion detection model of industrial control network is built based on the CSWC-SVM algorithm. Finally, KDD CUP 1999 data are introduced to carry out simulation experiments on the algorithm model constructed in this study in the virtual reality simulation environment. The results show when the penalty factor of the polynomial kernel function, radial basis kernel function, and sigmoid kernel function is 104, the average number of support vectors is 45, 46, and 37, respectively; the average training time are about 0.43, 0.45, and 0.47 s, and the average test time is about 9.7, 9.9, and 10.2 s, respectively; the average recognition accuracy is about 85.7%, 86.2%, and 86.7%, and the false positive rate is 3.8%, 2.8%, and 2.3%, respectively; the accuracy of the CSWC-SVM algorithm in different sample sizes (1000-6000) can be kept above 90%. The operation error rate of the CSWC-SVM algorithm is lower than that of C-SVM, C-SVM, and RS-SVM algorithms under different validation data sets. After dimension reduction, the classification accuracy of the CSWC-SVM algorithm is higher than that of C-SVM and WC-SVM algorithms. The weight value increases from 0 to 200, and the number of model errors on 1000, 2000, and 3000 pieces of data decreases significantly. When the weight value is 200, the number of errors drops to 0, and the classification accuracy reaches 100%. In a word, the CSWC-SVM algorithm constructed in this study performs well in response to the attack of the industrial control system in the virtual reality simulation environment, which provides practical significance for the application of virtual reality in industrial monitoring.