Application Study of IP AI Network in Computer Database Security Monitoring System

With the rapid development of the Internet, network attacks have gradually become automated and scaled, rendering traditional detection methods inadequate. This paper emphasizes the application and significant impact of IP AI networks in computer database security monitoring systems. The intelligent IP network system constructed leverages intrusion detection technology (including supervised and unsupervised learning algorithms) grounded in artificial intelligence to achieve intelligent monitoring. Simulated SQL injection and DDoS attacks demonstrate that the IP AI network achieves an average intrusion detection accuracy of 95.5%, significantly surpassing the 77.5% of traditional systems, with an average detection time of 0.65 seconds, much lower than the 1.7 seconds of traditional systems. Furthermore, the average recognition accuracy for abnormal behavior is 91.5%, higher than the 69.5% of traditional systems, and the correlation coefficient between the security situation score and actual security events is 0.87, compared to only 0.45 for traditional systems. It is evident that IP AI networks exhibit remarkable advantages in detection, response, and situational awareness, effectively safeguarding database security.