kumoh national institute of technology
Networked Systems Lab.

Amaizu Gabriel, Jae-Min Lee and Dong-Seong Kim "SDNAD: A Software Defined Network Anomaly Detection Model using Improved Deep Neural Network", Journal of Computer Science and Technology - 2020 (S)
By : Gabriel
Date : 2019-12-28
Views : 238

There has been a tremendous rise in the acceptance as well as incorporation of Software Defined Networking (SDN) into the Internet since it was first introduces. With SDN, it is now possible for us to have a global view of the network which in turn allows for easy maintenance and administration unlike before. This however comes with a lot of security challenges since in SDN a logically centralized controller is used to control the network. In this paper we present a Software Defined Network Anomaly Detector (SDNAD), a model that we built, trained and tested using an enhanced ANN.