Application of Deep Learning in Public Network Security Management

Abstract

With the rapid advancement of information technology, network security issues have become increasingly prominent, posing significant challenges in the field of public administration. Deep learning, as a cutting-edge technology in artificial intelligence, is emerging as a critical tool for enhancing network security management due to its exceptional performance in big data processing and pattern recognition. This paper reviews the basic concepts of deep learning and its current applications in network security management, discusses the design and implementation of deep learning-based network security management systems, and analyzes their effectiveness and evaluation through specific application cases. The results show that deep learning excels in areas such as intrusion detection, malware detection, network traffic analysis, and anomaly detection, significantly enhancing network security defenses. However, the application of deep learning in network security still faces challenges such as data privacy, model robustness, and computational resources. The paper concludes by proposing future development directions, providing theoretical support and practical references for further improving public network security management.