A Survey on Intrusion Detection System for Software Defined Networks (SDN)

• Published in: International journal of business data communications and networking Vol. 16; no. 1; pp. 28 - 47
• Main Authors: Hande, Yogita, Muddana, Akkalashmi
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2020
• Subjects: Communications software; Comparative studies; Deep learning; Intrusion detection systems; Machine learning; Security; Software-defined networking; Surveys; India
• ISSN: 1548-0631; 1548-064X
• DOI: 10.4018/IJBDCN.2020010103

Presently, the advances of the internet towards a wide-spread growth and the static nature of traditional networks has limited capacity to cope with organizational business needs. The new network architecture software defined networking (SDN) appeared to address these challenges and provides distinctive features. However, these programmable and centralized approaches of SDN face new security challenges which demand innovative security mechanisms like intrusion detection systems (IDS's). The IDS of SDN are designed currently with a machine learning approach; however, a deep learning approach is also being explored to achieve better efficiency and accuracy. In this article, an overview of the SDN with its security concern and IDS as a security solution is explained. A survey of existing security solutions designed to secure the SDN, and a comparative study of various IDS approaches based on a deep learning model and machine learning methods are discussed in the article. Finally, we describe future directions for SDN security.