Design and Implementation of Computer Network Security Defense System Based on Artificial Intelligence Technology

With the rapid development of Internet technology, the security threats faced by computer networks are becoming increasingly complex and diverse, and traditional defense methods can no longer effectively respond to new types of attacks. To address this problem, this study designs and implements an advanced computer network security defense system based on Deep Convolutional Neural Networks (DCNN) in artificial intelligence. First, DCNN is used to monitor network traffic data in real time and extract features to identify potential abnormal behaviors. Then, the trained DCNN model is used to accurately classify and predict detected threats, thereby distinguishing different types of network attacks. Finally, natural language processing technology is combined to achieve automated response and handling of security incidents, and by integrating multi-level defense strategies, a comprehensive security protection system is built to effectively improve overall defense capabilities. In the experimental conclusion, the DCNN model achieved accuracy, recall, and F1 score of 95%, 92%, and 93%, respectively, in threat detection. In addition, the response time of the DCNN system under high-intensity attacks is only 0.90 s, which is significantly lower than the traditional intrusion detection system (IDS) and SVM model. The system in this study shows good adaptability in various attack scenarios, ensuring stability and reliability in complex network environments. Therefore, the network security defense system based on DCNN has strong application prospects and practical value.