Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN.

翻译：软件定义网络（SDN）为智慧城市的网络建设提供了技术支持，然而SDN的开放性也使其更容易遭受网络攻击。传统的异常流量检测方法算法复杂且难以实时发现网络异常，无法满足SDN环境下的异常检测需求。为此，我们提出了一种基于深度学习混合模型的异常流量检测系统。该系统采用分层检测技术，首先基于端口信息实现异常流量的粗检测，随后利用小波变换与深度学习技术对经过可疑交换机的所有流量数据进行精细检测。实验结果表明，基于端口信息的检测方法能快速完成异常流量源的近似定位，精细检测的准确率、精确率和召回率相比传统SDN异常流量检测方法均有显著提升。