Research Area: Software Defined Networks
Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works.
Keywords:
NIDS
Machine learning
Deep learning
SDN
Author(s) Name: Nasrin Sultana, Naveen Chilamkurti, Wei Peng & Rabei Alhadad
Journal name: Peer-to-Peer Networking and Applications
Conferrence name:
Publisher name: IEEE
DOI: 10.1007/s12083-017-0630-0
Volume Information: volume 12, pages 493–501 (2019)
