University of Oulu

A. Ahmad, E. Harjula, M. Ylianttila and I. Ahmad, "Evaluation of Machine Learning Techniques for Security in SDN," 2020 IEEE Globecom Workshops (GC Wkshps, 2020, pp. 1-6, doi: 10.1109/GCWkshps50303.2020.9367477

Evaluation of machine learning techniques for security in SDN

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Author: Ahmad, Ahnaf1; Harjula, Erkki1; Ylianttila, Mika1;
Organizations: 1University of Oulu, Oulu, Finland
2VTT Technical Research Center of Finland, Espoo, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021051129508
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-05-11
Description:

Abstract

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.

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ISBN: 978-1-7281-7307-8
ISBN Print: 978-1-7281-7308-5
Pages: 1 - 6
DOI: 10.1109/GCWkshps50303.2020.9367477
OADOI: https://oadoi.org/10.1109/GCWkshps50303.2020.9367477
Host publication: 2020 IEEE Globecom Workshops (GC Wkshps) : Proceedings. Virtual Conference, 7-11- Dec 2020
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
113 Computer and information sciences
Subjects:
IDS
SDN
Funding: This work has been supported by TEKES Finland and Academy of Finland under projects: 6Genesis Flagship (grant 318927), and 5GEAR (Grant No. 319669) projects, and SecureConnect.
Academy of Finland Grant Number: 318927
319669
Detailed Information: 318927 (Academy of Finland Funding decision)
319669 (Academy of Finland Funding decision)
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