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 computernetworks more flexible. Although the SDN application on Google's internal network demonstrates the usefulnessof the Software-Defined Network approach and the promise of future technology, security is a vital concern thatcannot be ignored. In the SDN architecture, the attacker can now attack the network from any of the three planesbecause the Data Plane is separated from the Control Plane. Machine learning algorithms are methods used todetect attacks and intrusions on computer networks and can also be used for SDN. In this study, a new testbed hasbeen implemented for anomaly detection using machine learning algorithms in SDN. The developed systemanalyzes flows passing through the OpenFlow supported switch and tries to detect abnormal situations using thedecision tree machine learning algorithm. The results show that the system constructed using the decision treealgorithm works successfully against Distributed Denial of Service (DDoS) attacks.

Keywords

• yazılım tanımlı ağ
• anomali tespiti
• makine öğrenimi
• pox
• anomaly detection
• machine learning
• software defined networking
• pox controller