A Deep Learning Approach to Detect Anomaly in Software-Defined Network
Xinshuo Bai, Jun Bai, Yang Xu, Hongri Liu, Bailing Wang, Yang Liu · 2020
Software-Defined Network (SDN), an innovating technique, endows flexibility and programmability to the network in contrast to the conventional one. Its idea of decoupling control and data plane brings convenience to manage networks like load balancing, traffic engineering, virtual firewall etc. Nevertheless, the separated plane structure results in novel threats, composed by the common in conventional network and the specialized in SDN. In this paper, we propose a deep learning approach to detect anomaly in SDN with OpenFlow by analyzing multiple metrics extracted from OpenFlow switch metadata. Evaluated by four trained deep learning models to classify the multivariate time series, we obtained an average accuracy of 83.8%.