Usage of Machine Learning Algorithms for Flow Based Anomaly Detection System in Software Defined Networks
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Computer networks are becoming more complex in the number of connected nodes and the amount of traffic. The growing number and increasing complexity of cyber-attacks makes network management and security a challenge. Software defined networks (SDN) technology is a solution that aims for efficient and flexible network management. The SDN controller(s) plays an important role in detecting and preventing cyber-attacks. In this study, a flow-based anomaly detection system running on the POX controller is designed. A comparative analysis of the supervised machine algorithms is given to choose the optimum anomaly detection method in SDN based networks. NSL-KDD dataset is used for training and testing of the classifiers. The results show that machine learning algorithms have great potential in the success of flow-based anomaly detection systems in the SDN infrastructure. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.