Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach

Tang, TA, McLernon, D orcid.org/0000-0002-5163-1975, Mhamdi, L et al. (2 more authors) (2019) Intrusion Detection in SDN-Based Networks: Deep Recurrent Neural Network Approach. In: Alazab, M and Tang, M, (eds.) Deep Learning Applications for Cyber Security. Advanced Sciences and Technologies for Security Applications . Springer , Cham, Switzerland , pp. 175-195. ISBN 978-3-030-13056-5

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Copyright, Publisher and Additional Information: © 2019, Springer Nature Switzerland AG. This is an author produced version of a book chapter published in Deep Learning Applications for Cyber Security. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: SDN; Software-defined networking; Network security; Network intrusion detection; Machine learning; Deep learning
Dates:
  • Published: 15 August 2019
  • Accepted: 19 September 2018
  • Published (online): 15 August 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 21 Feb 2019 13:22
Last Modified: 30 Jun 2020 14:49
Status: Published
Publisher: Springer
Series Name: Advanced Sciences and Technologies for Security Applications
Identification Number: https://doi.org/10.1007/978-3-030-13057-2_8

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Filename: Tuan Tang_Final Book Chapter Manuscript.pdf

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