Asia-Pacific Network Operations and Management Symposium
Deep Learning Based Anomaly Detection Scheme in Software-Defined Networking
Yang Qin, Junjie Wei, Weihong Yang,
PDF download (357.8KB)
Software Defined Networking (SDN) has attracted more and more attention due to its prominent features that are different from the traditional network. SDN is programmable through which controller can modify the rules in the switch. However, security was not considered in its initial design, and many manufacturers no longer support Transport Layer Security (TLS) due to the cost. Although many machine learning based approaches have been implemented in SDN, they all need features that experts extract from original data. However, the manual extraction increases the level of human interaction and decreases detection accurate. This paper presents a malicious network traffic classification method based on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to address these concerns. Our proposed method is implemented in Graphic Process Unit (GPU) enabled TensorFlow. We evaluated our proposal on three datasets. The results demonstrate that our proposal achieves improvements in term of detection accuracy and stability over existing approaches and strong potential for user in SDN security.