Title: Financial data security risk perception of cloud platform based on graph neural network

Authors: Kejing Lu

Addresses: School of Accounting and Finance, Jiangsu Vocational College of Business, Nantong, 226000, China

Abstract: To address the challenges of suboptimal precision and extended duration associated with conventional approaches to security risk detection, a financial data security risk perception of cloud platform based on graph neural network is proposed. Financial data are collected from cloud platforms and financial data security risk perception indicators are constructed. By filling in missing values, removing outliers, and normalising data, the data is processed. The efficiency indicators are ranked in ascending order, and the cost indicators are ranked in descending order. The rank sum ratio of each indicator is calculated, and the entropy weight method is used to calculate the indicator weights. The trained graph neural network is used to achieve the perception of financial data security risks on the cloud platform. The findings from our experiments indicate that our approach achieves a security risk perception accuracy of 94.3%, with a time consumption of only 2.3 s, and the application effect is good.

Keywords: graph neural network; cloud platform; financial data; security risk perception; rank sum ratio; entropy weight method.

DOI: 10.1504/IJITM.2025.151553

International Journal of Information Technology and Management, 2025 Vol.24 No.3/4, pp.204 - 221

Received: 02 Sep 2024
Accepted: 07 Feb 2025
Published online: 05 Feb 2026 *

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