Supervised machine-learning for SDN-based intrusion detection task.

What is it about?

The integration between software-defined networking and machine learning helps in creating more secure networks. Too many machine learning algorithms can be used for intrusion detection task. Our study focuses on comparing well-known supervised machine learning approaches for building an efficient SDN-based intrusion detection system.

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Why is it important?

Our work provides a detailed analysis as the well-known supervised machine-learning algorithms were compared in terms of accuracy, false alarm rate, precision, recall, F1-measure, area under ROC curve, Mc Nemar's test, and the time taken to train and test the model. In addition, we employed the Principle Component Analysis (PCA) approach for feature selection and dimensionality reduction which PCA helped in enhancing the accuracy level from 80.31% to 88.74% in compared with using the basic features provided by the SDN controller.

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