DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Tang, TA, Mhamdi, L, McLernon, D et al. (3 more authors) (2020) DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking. Electronics, 9 (9). 1533. pp. 1-18. ISSN 2079-9292

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: deep learning; intrusion detection; network security; software defined networking; SDN
Dates:
  • Published: 19 September 2020
  • Accepted: 10 September 2020
  • Published (online): 19 September 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
Royal Academy of EngineeringNot Known
Depositing User: Symplectic Publications
Date Deposited: 01 Oct 2020 10:52
Last Modified: 01 Oct 2020 10:52
Status: Published
Publisher: MDPI
Identification Number: https://doi.org/10.3390/electronics9091533

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