Citation

Zhou X, Luo W, Liu J, Xie B. China Saf. Sci. J. 2022; 32(8): 52-60.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.08.2483

PMID

unavailable

Abstract

In order to find potential information security risks of smart cities in time, an information security risk prediction model was built based on IIWPSO algorithm optimized BP (IIWPSO-BP) neural network algorithm. Firstly, the information security risk index system was constructed by considering six aspects: information owner, shared information, alliance chain technology, information user, alliance chain management and security measures. Secondly, the information security risk prediction model was trained and tested by quantifying the information security risk index. Finally, the robustness, accuracy and time complexity of the model were compared and analyzed. The results show that the mean absolute error (MAE) of the IIWPSO-BP prediction model is 0. 137 4, the mean relative error (MRE) is 0. 038 5, and the fitting degree is 0. 972 0. The prediction accuracy is improved by 37. 6% and 65. 2%, respectively, compared with the PSO-BP neural network and the BP neural network. © 2022 China Safety Science Journal. All rights reserved.

Language: zh