【摘要】 软件定义网络(SDN)是一种将控制层和数据层分离的新型网络架构，在实现网络集中管理和可编程性的同时也面临易受到入侵攻击的问题。针对此问题设计了检测防御机制。利用深度学习算法，对数据集进行处理后，融合卷积神经网络(CNN)和双向长短期记忆网络(BiLSTM),设计了CNN-BiLSTM模型检测攻击，利用SDN可编程性设计了防御机制，搭建基于SDN的网络平台进行仿真实验。实验结果表明，所设计方法相较传统检测方法可更准确检测出入侵流量，并在检测出后有效实现了防御功能。更多还原

【Abstract】 Software Defined Network(SDN) is a new network architecture that separates the control layer from the data layer. While realizing centralized management and programmability of the network, it also faces the problem of vulnerability to intrusion attacks. A detection and defense mechanism is designed for this problem. After using the deep learning algorithm to process the data set, the CNN-BiLSTM model is designed to detect attacks by integrating the convolutional neural network(CNN) and the bidirectional long-term and short-term memory network(BiLSTM), and the defense mechanism is designed by using SDN programmability. A network platform based on SDN is built for simulation experiments. Experimental results show that the designed method can detect intrusion traffic more accurately than traditional detection methods and can effectively implement defense functions after detection.更多还原