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Deep learning approach for Network Intrusion Detection in Software Defined Networking

Tuan Anh TangSchool of Electronic and Electrical Engineering, The University of Leeds, Leeds, UKLotfi MhamdiSchool of Electronic and Electrical Engineering, The University of Leeds, Leeds, UKDes McLernonSchool of Electronic and Electrical Engineering, The University of Leeds, Leeds, UKSyed Ali Raza ZaidiSchool of Electronic and Electrical Engineering, The University of Leeds, Leeds, UKMounir GhoghoInternational University of Rabat, Morocco

2016en

ABI

Annotatsiya

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.

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DOI: 10.1109/wincom.2016.7777224

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