kumoh national institute of technology
Networked Systems Lab.

Ali Aouto, William-Paul Nwadiugwa, Jae-Min Lee and Dong-Seong Kim, "Intrusion Detection using Deep Learning Technique for Industrial SDN", 2019 KICS Summer Conference, pp. 1528-1530, June 19-21 2019, Jeju Island, South Korea (N2).
By :
Date : 2019-04-04
Views : 355

The recent enhancement in the Internet Technology area has shown the necessity for intelligence inside 
networks, which could be done by Software Defined Network (SDN). The features that SDN bears would give 
a chance to reinforce the network security. On the other hand, SDN also holds a possible threat in the 
intrusion probability. This paper discusses the application of deep learning to a flow-based anomaly 
detection in an SDN environment. The Deep Neural Network (DNN) model is built to detect the intrusion, 
after that it will train the model by using the NSL-KDD dataset. This paper only discusses 6 basic 
features of NSL-KDD dataset, confirmation of the approach would be shown by some experiments.