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Attack Classification Using Machine Learning Techniques in Software-Defined Networking

Abstract:

Software-defined networking represents a novel network model that separates control functionality from data management, significantly enhancing the latter’s efficiency and flexibility. Nevertheless, it faces substantial security threats that jeopardize data and service availability. This paper aims to define a model for classifying attacks using machine learning techniques to enhance defense capabilities and bolster data management security in software-defined networking. The classifier was trained with three machine learning algorithms: decision trees, random forests, and support vector machines, applying various feature sets from two public datasets with software-define networking traffic. In the training phase, 99.76%, 99.75%, and 99.50% accuracy rates were achieved for decision trees, random forests, and support vector machines, respectively. Consequently, the results obtained in this study outperform state-of-the-art approaches and demonstrate the successful deployment of a machine learning model in a software-defined networking environment.