A Feature Selection Method Based On Software Defined Networks

Abstract

Software-defined networking is in the midst of information security challenges. In the case that distributed denial of service attacks occur against a software-defined network controller, network traffic data becomes vulnerable because of the overload. In this study, distributed denial of service attacks in software defined network are detected by using machine learning based models with different datasets. First, certain features are obtained on the software-defined network for the dataset under normal conditions and under attack traffic. Subsequently, a new data set was generated by using the proposed hybrid feature selection method on the existing data set. The proposed feature selection method algorithm is trained and tested with logistic regression, artificial neural network, k-nearest neighbors and Naive Bayes models. The results show that the use of the hybrid method has positive impacts on the performance metrics and provides lower processing time.