Application of Big Data Association Rule Algorithm in Accounting Network Security Monitoring and Accounting System

Abstract

In complex environments such as computers, networks, and big data, the integrity and security issues of accounting information have become increasingly prominent. This study analyzes existing vulnerabilities in financial systems to identify specific security and cyber threats that challenge data integrity in the financial industry. In response to the above problems, this paper applies a new network security monitoring technology based on association rules. With the advantages of big data analysis, this traditional monitoring method has been further strengthened, allowing it to better realize real-time monitoring of financial data. Through early detection of security incidents, potential risks are reduced and the normal operation of the accounting system is ensured. Experiments have proven that the enhanced monitoring system has made great improvements in the identification and statistics of network security incidents. There is an average accuracy rate of 95%, which shows that the system is reliable and has the ability to enhance safety measurements. Organically integrate big data technology and traditional network security monitoring methods to form a robust network security protection mechanism.