What question did this study set out to answer?

This research aims to enhance network security in Software Defined Networking (SDN) by applying deep learning techniques for anomaly detection.

October 1, 2016

Deep learning approach for Network Intrusion Detection in Software Defined Networking

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Randomized trial demonstrates effective anomaly detection in software defined networks, highlighting the need for robust security solutions.

This research aims to enhance network security in Software Defined Networking (SDN) by applying deep learning techniques for anomaly detection.
Developed a Deep Neural Network (DNN) model for intrusion detection.
Trained the model using the NSL-KDD Dataset focusing on six key features.
Employed flow-based anomaly detection techniques specific to SDN.
The deep learning model effectively detected anomalies in flow-based data.
Designed model achieved strong performance metrics, confirming its potential for improving SDN security.

Tang et al. (2016) studied this question.

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Randomized trial demonstrates effective anomaly detection in software defined networks, highlighting the need for robust security solutions.