Detection and analysis of network system security based on machine learning

Abstract

Computer networks (CNs) has been widely popularized and applied. They not only affect people’s daily lives, but also promote the development of the times and society. However, in the era of big data (BD), the rapid growth of data poses significant challenges to computer network security management (CNSM). The large amount, fast speed, and diverse types of data generated by modern networks make it increasingly difficult for security professionals to detect and respond to threats in real-time. Artificial intelligence (AI) has the potential to play an important role in CNSM in the era of BD. It can quickly analyze large amounts of data, automate daily tasks, and predict potential network vulnerabilities. This article conducted relevant research on the development of CNSM technology based on AI technology. The final experimental results showed that the average accuracy score of network security (NS) detection based on AI technology is 92.35 points; the average overall event response time is 9.45 hours; the average cost of network security management (NSM) is 147300 US dollars. These indicators have huge advantages compared to traditional NSM technology.