Deep Learning Approach for Network Intrusion Detection in Software Defined Networking

Tang, TA, Mhamdi, L, McLernon, D et al. (2 more authors) (2016) Deep Learning Approach for Network Intrusion Detection in Software Defined Networking. In: 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM). The International Conference on Wireless Networks and Mobile Communications (WINCOM'16), 26-29 Oct 2016, Fez, Morocco. IEEE . ISBN 978-1-5090-3837-4

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Copyright, Publisher and Additional Information: © 2016, IEEE. This is an author produced version of a paper accepted for publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy.
Keywords: network security; software defined networking; SDN; intrusion detection; deep learning
Dates:
  • Published: 8 December 2016
  • Accepted: 19 October 2016
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)
Depositing User: Symplectic Publications
Date Deposited: 04 Nov 2016 10:35
Last Modified: 10 Apr 2017 17:05
Published Version: https://doi.org/10.1109/WINCOM.2016.7777224
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/WINCOM.2016.7777224

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