Analysis of Machine Learning for Securing Software-Defined Networking

Hassan A. Alamri*, Vijey Thayananthan

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)
15 Downloads (Pure)


Advancements in Software-Defined Networking (SDN) are growing with the emerging network architectures which face the security issues and challenges when adversary launches severe attacks such as Distributed Denial of Service (DDoS) attacks. Therefore, a considerable number of solutions have been devised to alleviate DDoS attacks in SDN using a machine learning approach. Thus, the aim of this study is to review and analyze the machine learning-based schemes for securing the SDN environment targeted by DDoS attacks. The schemes' method, performance metrics, datasets, and other remarks such as benchmarks, strengths, and weaknesses are discussed. The CIC-DDoS 2019 dataset was utilized to evaluate the performance of a set of classification algorithms that are widely used in machine learning-based DDoS attack detection in the SDN environment. Finally, challenges and future directions in the development of machine learning-based detection schemes in SDN are discussed.

Original languageEnglish
Pages (from-to)229-236
Number of pages8
JournalProcedia Computer Science
Publication statusPublished - 3 Dec 2021
Externally publishedYes
Event18th International Learning and Technology Conference 2021 - Virtual, Online, Saudi Arabia
Duration: 28 Jan 202128 Jan 2021
Conference number: 18th


  • Distributed Denial of Service (DDoS) Attack
  • Machine Learning Algorithm
  • Security
  • Software-Defined Networking (SDN)


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