Title: An identification method of digital economy security risk dimension based on Bayesian network

Authors: Rong Xia; Xueqin Ni

Addresses: School of Economics and Management, Wuhan Railway Vocational College of Technology, Wuhan, 430000, China ' School of Economics and Management, Wuhan Railway Vocational College of Technology, Wuhan, 430000, China

Abstract: In order to improve the recall rate and accuracy rate of digital economy security risk identification and reduce the identification time, this paper proposes a Bayesian network based risk dimension identification method for digital economy security. Firstly, the principle of establishing an evaluation indicator system is to screen risk indicators through the boundary value method to obtain a risk indicator system; then, K-means clustering is used to classify the risk levels and construct a risk identification function; finally, Bayesian network learning is used to solve the identification function and achieve the identification of security risk dimensions. The results show that the recall rate under this method is always higher than 98%, the accuracy of risk dimension identification is always higher than 93%, and the time required for risk dimension identification does not exceed 5.0 seconds, indicating that this method can effectively improve the effectiveness of risk dimension identification.

Keywords: Bayesian network; Bayesian network learning; boundary value method; identification function.

DOI: 10.1504/IJSD.2024.136612

International Journal of Sustainable Development, 2024 Vol.27 No.1/2, pp.1 - 15

Received: 30 May 2023
Accepted: 30 Aug 2023
Published online: 08 Feb 2024 *

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