Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Tang, TA, Mhamdi, L, McLernon, D orcid.org/0000-0002-5163-1975 et al. (2 more authors) (2018) Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). NetSoft 2018: 4th IEEE Conference on Network Softwarization and Workshops, 25-29 Jun 2018, Montreal, QC, Canada. IEEE , pp. 202-206. ISBN 978-1-5386-4633-5

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2018 IEEE. This is an author produced version of a paper published in 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: software defined networking; SDN; intrusion detection; deep learning; recurrent neural network; gated recurrent unit; GRU; network security
Dates:
  • Published: 13 September 2018
  • Accepted: 26 March 2018
  • Published (online): 13 September 2018
Institution: The University of Leeds
Depositing User: Symplectic Publications
Date Deposited: 29 Mar 2018 09:49
Last Modified: 24 Oct 2018 10:43
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/NETSOFT.2018.8460090

Share / Export

Statistics