Intelligent cluster construction of internet financial security protection system in banking industry

Author:

Wang Yanzhao1

Affiliation:

1. Henan Institute of Economics and Trade, College of Finance , Zhengzhou 450018, Henan , China

Abstract

Abstract As the rapid advancement of information security company is developing quickly and the technology including big information, big computer, large cloud, and artificial intelligence are being widely used, network security has entered a new era. While ushering in huge development opportunities, it also faces severe tests. Network security is a major issue related to the comprehensive realization of a well-off society and national security, and has risen to the national strategic level. The current financial industry network security construction mainly focuses on team, process construction, and the research and development of individual tools and equipment. It lacks system research and implementation guidelines for defense technology construction based on industry IT characteristics. At the same time, there is also a lack of objective and unified measurement and evaluation standards for enterprise security defense capabilities, which restricts the improvement of cybersecurity capabilities in the financial industry to a certain extent. In terms of actual combat exercises, in various actual combat exercises over the years, the technical architecture of the bank’s network security defense has withstood the test of high-intensity confrontation. The defense process achieved zero deductions, and stood out among the participating defending teams through traceability and countermeasures, which effectively improved the network security large-scale group operations and security protection capabilities. The effectiveness of the technical architecture design is verified in actual combat, which shows that the control of the bank’s internet financial security protection system is effective. With digital computing, all banking transactions are fully automatically implemented and the bank’s clientele is systematically self-managed. It predicts that using this system, the speed can be increased by 98% and the accuracy can be increased by 12%.

Publisher

Walter de Gruyter GmbH

Subject

General Computer Science

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