Network attack pattern recognition and early warning based on multimodal data and artificial intelligence

This study proposes an innovative method for recognizing network attack patterns and issuing early warnings that combines multimodal data processing with advanced artificial intelligence technology. By comprehensively analyzing multiple data sources, including traffic, logs, and events, and applying deep learning models, the efficient identification of complex network attack behaviors is achieved. At the same time, ensemble learning and knowledge graphs are introduced to enhance the system’s stability and accuracy, as well as improve model interpretability, providing an intuitive support tool for network security managers. Experimental results show that, compared to traditional algorithms, this new method has achieved significant progress in key performance indicators, particularly in addressing high-dimensional and data imbalance problems. This demonstrates that integrating multimodal data with AI technology is a practical approach to addressing contemporary network security challenges. Future work will focus on optimizing existing models, exploring additional data types, and enhancing the system’s adaptability to dynamic network environments, thereby continuously improving the effectiveness of network attack detection and response efficiency.