Deep learning approach for Network Intrusion Detection in Software Defined Networking
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty one features of NSL KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow based anomaly detection in SDN environments.
2016 International Conference on Wireless Networks and Mobile Communications (WINCOM)