Abnormal traffic detection system in SDN based on deep learning hybrid models

Abstract

Software defined network (SDN) provides technical support for network construction of smart cities. However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection algorithms are complex and time-consuming, so it is difficult to find abnormalities in the network in time and unable to satisfy the requirements of abnormal traffic 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 method. Firstly, it completes the rough detection of abnormal traffic in the network according to the statistical information of switch ports and then uses wavelet transform and deep learning technology to extract multi-dimensional features of all traffic data flowing through suspicious switches, so as to realize the fine detection of abnormal traffic from the surface. The experimental results show that the proposed detection method based on port information can quickly locate the source of abnormal traffic. Compared with the traditional abnormal traffic detection method in SDN, the fine detection method based on multi-dimensional features improves the accuracy by 1.7 %, the recall rate by 1.6 %, and the false positive rate by 91.3 %.