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Detection of DDoS Attack Using Machine Learning Techniques in Software Defined Networking

Detection of DDoS Attack Using Machine Learning Techniques in Software Defined Networking

Muthamil Sudar K., Ruba Soundar K., Vinoth P., Nagaraj P., Muneeswaran V.
Copyright: © 2023 |Pages: 18
ISBN13: 9781668460924|ISBN10: 1668460920|ISBN13 Softcover: 9781668460931|EISBN13: 9781668460948
DOI: 10.4018/978-1-6684-6092-4.ch002
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MLA

Sudar K., Muthamil, et al. "Detection of DDoS Attack Using Machine Learning Techniques in Software Defined Networking." Handbook of Research on Current Trends in Cybersecurity and Educational Technology, edited by Remberto Jimenez and Veronica E. O'Neill, IGI Global, 2023, pp. 19-36. https://doi.org/10.4018/978-1-6684-6092-4.ch002

APA

Sudar K., M., K., R. S., P., V., P., N., & V., M. (2023). Detection of DDoS Attack Using Machine Learning Techniques in Software Defined Networking. In R. Jimenez & V. O'Neill (Eds.), Handbook of Research on Current Trends in Cybersecurity and Educational Technology (pp. 19-36). IGI Global. https://doi.org/10.4018/978-1-6684-6092-4.ch002

Chicago

Sudar K., Muthamil, et al. "Detection of DDoS Attack Using Machine Learning Techniques in Software Defined Networking." In Handbook of Research on Current Trends in Cybersecurity and Educational Technology, edited by Remberto Jimenez and Veronica E. O'Neill, 19-36. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-6092-4.ch002

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Abstract

The software-defined network (SDN) has recently developed as a network paradigm due to its high network programmability and flexibility, which can overcome the difficulty in traditional networks by detaching the control plane from the data plane. Following the controller's decision in the control plane, the data plane will transfer the packets. This unified administration will make it possible to see the network architecture as a whole in an abstract way. The controller is exposed to a significant threat if control is lost due to its centralised structure. Data plane resources are attacked by the attacker by focusing on switches that support OpenFlow. DDoS attacks damage network performance by overloading the SDN controller and network links, depleting the victim's bandwidth, and flooding the server with massive amounts of data. To address this issue, the authors employ statistical-based and machine learning-based techniques in SDN controller to inspect the new incoming flows.

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