深度学习在软件定义网络研究中的应用综述

数据转发与控制分离的软件定义网络（Software Defined Networking，简称SDN）是对传统网络架构的彻底颠覆，为网络各方面的研究引入新的机遇和挑战.随着传统网络研究方法在SDN中遭遇瓶颈，基于深度学习的方法被引入到SDN的研究中，在实现实时智能的网络管控上成果颇丰，推动了SDN研究的深入发展.调查了深度学习开发平台，训练数据集，智能SDN架构等深度学习引入SDN的促进因素；对智能路由，入侵检测，流量感知和其他应用等SDN研究领域中的深度学习应用进行系统的介绍，深入分析了现有深度学习应用的特点和不足；最后展望了SDN未来的研究方向与趋势.

Survey on Deep Learning Applicatons in Software Defined Networking Research

Software Defined Networking (SDN), which separates data forwarding from control, is a complete overthrow of traditional network architecture, introducing new opportunities and challenges for all aspects of network research. With the traditional network research methods encountering bottlenecks in SDN, deep learning based methods have been introduced into the research of SDN, resulting in plenty of achievements in real-time intelligent network management and control, which promotes the further development of SDN research. This paper investigates the promoting factors of introducing deep learning into SDN, such as deep learning development platform, training datasets and intelligent SDN architectures; introduces the deep learning applications in SDN research fields such as intelligent routing, intrusion detection, traffic perception and other applications systematically, and analyzes the features and shortcomings of those deep learning applications in detail; Finally, prospects the future research direction and trend of SDN.