ISSN E 2409-2770
ISSN P 2521-2419

Intrusion Detection System for SDN based IoT Devices using Deep Neural Network


Vol. 7, Issue 09, PP. 293-297, September 2020


Keywords: Internet of Things, Software-defined networking, anomoly detection, machine learning

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One of the emerging technologies in the field of networking is the Software Defined Networking (SDN). Since it is a centrally controlled networks, it provides us with a better control to improve the security within our network against the potential threats. In this work we are using Deep Neural Network (DNN) model to detect the flow-based anomaly within the network. The model was trained on NSL-KDD dataset and out of forty-one features only six of the most relevant features of NSL-KDD were used. The results show that Deep Learning approach shows some promising results in detecting the anomaly in the SDN environment.

  1. Naqib Ullah , , Abasyn University, Peshawar, Pakistan.
  2. Abdus Salam , , Abasyn University, Peshawar, Pakistan.

Naqib Ullah Dr. Abdus Salam Intrusion Detection System for SDN based IoT Devices using Deep Neural Network International Journal of Engineering Works Vol. 7 Issue 09 PP. 293-297 September 2020

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