Mhamdi, L, McLernon, D orcid.org/0000-0002-5163-1975, El-moussa, F et al. (3 more authors) (2021) A Deep Learning Approach Combining Auto-encoder with One-class SVM for DDoS Attack Detection in SDNs. In: 2020 IEEE Eighth International Conference on Communications and Networking (ComNet). 8th IEEE International Conference on Communications and Networking IEEE ComNet'2020, 27-30 Oct 2020, Hammamet, Tunisia. IEEE ISBN 978-1-7281-5321-6
Abstract
Software Defined Networking (SDN) provides us with the capability of collecting network traffic information and managing networks proactively. Therefore, SDN facilitates the promotion of more robust and secure networks. Recently, several Machine Learning (ML)/Deep Learning (DL) intrusion detection approaches have been proposed to secure SDN networks. Currently, most of the proposed ML/DL intrusion detection approaches are based on supervised learning approach that required labelled and well-balanced datasets for training. However, this is time intensive and require significant human expertise to curate these datasets. These approaches cannot deal well with imbalanced and unlabeled datasets. In this paper, we propose a hybrid unsupervised DL approach using the stack autoencoder and One-class Support Vector Machine (SAE-1SVM) for Distributed Denial of Service (DDoS) attack detection. The experimental results show that the proposed algorithm can achieve an average accuracy of 99.35 % with a small set of flow features. The SAE-1SVM shows that it can reduce the processing time significantly while maintaining a high detection rate. In summary, the SAE-1SVM can work well with imbalanced and unlabeled datasets and yield a high detection accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020, IEEE. All rights reserved. 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. |
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) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2020 12:21 |
Last Modified: | 08 Mar 2021 19:55 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ComNet47917.2020.9306073 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157807 |