Evaluation of Machine Learning Techniques for Security in SDN

Ahnaf Ahmad, Erkki Harjula, Mika Ylianttila, Ijaz Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

35 Citations (Scopus)


Software Defined Networking (SDN) has emerged as the most viable programmable network architecture to solve many challenges in legacy networks. SDN separates the network control plane from the data forwarding plane and logically centralizes the network control plane. The logically centralized control improves network management through global visibility of the network state. However, centralized control opens doors to security challenges. The SDN control platforms became the most attractive venues for Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Due to the success and inevitable benefits of Machine Learning (ML) in fingerprinting security vulnerabilities, this article proposes and evaluates ML techniques to counter DoS and DDoS attacks in SDN. The ML techniques are evaluated in a practical setup where the SDN controller is exposed to DDoS attacks to draw important conclusions for ML-based security of future communication networks.
Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
Subtitle of host publicationGC 2020 Workshop - SecSDN
PublisherIEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)978-1-7281-7307-8
ISBN (Print)978-1-7281-7308-5
Publication statusPublished - Dec 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference, GLOBECOM 2020: Online - Virtual, Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020


ConferenceIEEE Global Communications Conference, GLOBECOM 2020
Country/TerritoryTaiwan, Province of China


  • SDN
  • security
  • IDS
  • DDoS
  • machine learning
  • security in SDN


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