The Challenges in SDN/ML Based Network Security : A Survey
It addresses security challenges for network administrators and developers in SDN/ML systems, but is incremental as it synthesizes existing knowledge without new empirical results.
This survey examines the vulnerabilities in machine learning-based SDN security applications, highlighting the risks of compromising these models due to stealthier malicious activities and new technologies, and argues for more secure development processes.
Machine Learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking (SDN) emerge. Sitting at the application layer and communicating with the control layer, machine learning based SDN security models exercise a huge influence on the routing/switching of the entire SDN. Compromising the models is consequently a very desirable goal. Previous surveys have been done on either adversarial machine learning or the general vulnerabilities of SDNs but not both. Through examination of the latest ML-based SDN security applications and a good look at ML/SDN specific vulnerabilities accompanied by common attack methods on ML, this paper serves as a unique survey, making a case for more secure development processes of ML-based SDN security applications.