Design of Computer Network Security Risk Assessment System Based on Artificial Intelligence Technology

The application of artificial intelligence (AI) technology in the field of computer network security is becoming more and more important. This article presented a computer network security risk assessment system based on artificial intelligence technology, aiming at improving the accuracy and efficiency of risk assessment. The system design adopted advanced data collection, processing and analysis technology, combined with deep learning algorithms such as convolutional neural network, to realize real-time monitoring, accurate identification and adaptive response to network threats. Firstly, this article expounded the objectives of system design, including real-time performance, accuracy and adaptability. Then, the system architecture was constructed, including data acquisition layer, data processing and analysis layer and risk assessment and response layer. In the data processing and analysis layer, the system automatically extracted the characteristics of network traffic and used CNN to identify abnormal traffic patterns. In addition, the system also integrated various security strategies, including intrusion detection, abnormal behavior analysis, risk warning and disposal, etc. The experimental results showed that the designed system met high standards in response time, processing power, and stability. The system had fast response time, high throughput, and low fault rate, and demonstrated excellent performance. Compared with traditional risk assessment systems, CNN-based systems had significant advantages in risk assessment accuracy, with a maximum accuracy of 98.5%, while traditional systems had a maximum accuracy of only 77.4 %. The case study further demonstrates the effectiveness of the system in practical applications, especially in identifying complex network attacks and improving decision support.