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

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

Yogita Hande, Akkalashmi Muddana
Copyright: © 2021 |Pages: 23
ISBN13: 9781799877059|ISBN10: 1799877051|EISBN13: 9781799877486
DOI: 10.4018/978-1-7998-7705-9.ch023
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MLA

Hande, Yogita, and Akkalashmi Muddana. "A Survey on Intrusion Detection System for Software Defined Networks (SDN)." Research Anthology on Artificial Intelligence Applications in Security, edited by Information Resources Management Association, IGI Global, 2021, pp. 467-489. https://doi.org/10.4018/978-1-7998-7705-9.ch023

APA

Hande, Y. & Muddana, A. (2021). A Survey on Intrusion Detection System for Software Defined Networks (SDN). In I. Management Association (Ed.), Research Anthology on Artificial Intelligence Applications in Security (pp. 467-489). IGI Global. https://doi.org/10.4018/978-1-7998-7705-9.ch023

Chicago

Hande, Yogita, and Akkalashmi Muddana. "A Survey on Intrusion Detection System for Software Defined Networks (SDN)." In Research Anthology on Artificial Intelligence Applications in Security, edited by Information Resources Management Association, 467-489. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7705-9.ch023

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Abstract

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.

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