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
Abstract
Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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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: |
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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: | 10.1109/WINCOM.2016.7777224 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:106836 |