Research on computer network security situation awareness warning mechanism based on artificial intelligence

As computer network security threats become increasingly intricate, this paper centers on developing an AI-based network security situational awareness early warning mechanism to establish an accurate, real-time, and efficient defense system. By leveraging AI's prowess in big data processing, pattern recognition, and adaptive learning, the study enhances comprehensive understanding of network security situations, enabling timely detection and response to potential threats. The paper initially highlights the need and benefits of AI in network security situational awareness. Integrating deep learning, machine learning, and related technologies, the system accurately identifies various security incidents (e.g., abnormal traffic, malware, intrusions), adapts to dynamic network changes, and effectively combats novel, unknown threats. Subsequently, the paper presents an AI-driven network security situational awareness early warning model, consist of data acquisition, feature extraction, situation analysis, and early warning phases. Real-time network behavior data is gathered, deep neural networks extract key situation features, integrated learning constructs a risk assessment model, and automatic warnings trigger when risk thresholds are exceeded, accompanied by risk details and mitigation strategies. A prototype of the AI-based early warning system is designed and implemented. Experiments reveal its superior precision, minimal delay, and robust performance in large networks. Compared to traditional approaches, it improves threat detection by around 30%, reduces false alarms by over 40%, and significantly shortens response times. The system is scalable, adaptable to expanding network sizes and evolving attack tactics. Looking ahead, the paper explores future research topics like cross-domain data fusion, heterogeneous model collaboration, and human-computer interaction optimization, emphasizing the importance of considering legal, regulatory, ethical aspects in AI's application to network security. In conclusion, this study offers a systematic examination of AI-powered computer network security situational awareness warning mechanisms, providing a theoretical framework and practical guidance for building intelligent, proactive defense systems. It holds significant theoretical and practical value in enhancing national network space security capabilities.