Anomaly Detection in Software-Defined Networking Using Machine Learning

Abstract

In recent years, the Software-Defined Networking (SDN) approach has emerged that aims to make computer networks more flexible. Although the SDN application on Google's internal network demonstrates the usefulness of the Software-Defined Network approach and the promise of future technology, security is a vital concern that cannot be ignored. In the SDN architecture, the attacker can now attack the network from any of the three planes because the Data Plane is separated from the Control Plane. Machine learning algorithms are methods used to detect attacks and intrusions on computer networks and can also be used for SDN. In this study, a new testbed has been implemented for anomaly detection using machine learning algorithms in SDN. The developed system analyzes flows passing through the OpenFlow supported switch and tries to detect abnormal situations using the decision tree machine learning algorithm. The results show that the system constructed using the decision tree algorithm works successfully against Distributed Denial of Service (DDoS) attacks.

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