Survey on SDN based network intrusion detection system using machine learning approaches

• Published in: Peer-to-peer networking and applications Vol. 12; no. 2; pp. 493 - 501
• Main Authors: Sultana, Nasrin, Chilamkurti, Naveen, Peng, Wei, Alhadad, Rabei
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2019; Springer Nature B.V
• Subjects: Artificial intelligence; Challenges and Prospective Smart Solutions; Communications Engineering; Computer Communication Networks; Computer networks; Cybersecurity; Engineering; Environment models; Information Systems and Communication Service; Intrusion detection systems; Machine learning; Networks; Signal,Image and Speech Processing; Software-defined networking; Special Issue on Software Defined Networking: Trends; Deep learning; SDN; NIDS; Machine learning
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-017-0630-0

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.