Intrusion Detection with Machine Learning in Software Defined Networks
by
Derya Yıltaş Kaplan
Date and Time: November 20th, 2019 (Wednesday), 13:30
Place: Room Z23, Computer Engineering Building, GTU
All interested are cordially invited.
ABSTRACT:
The subject focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. This subject presents the referenced studies on the SDN security and artificial intelligence together.