Isa, M.M. and Mhamdi, L. orcid.org/0009-0000-6492-2088 (2022) Hybrid Deep Autoencoder with Random Forest in Native SDN Intrusion Detection Environment. In: ICC 2022 - IEEE International Conference on Communications. ICC 2022 - IEEE International Conference on Communications, 16-20 May 2022, Seoul, South Korea. IEEE , pp. 1698-1703. ISBN 9781538683477
Abstract
This paper introduces a hybrid deep autoencoder with a random forest classifier model to enhance intrusion detection performance in a native SDN environment. A deep learning architecture combining a deep autoencoder with random forest learning feature representation of traffic flows natively collected from the SDN environment. Publicly available packet Capture (PCAP) files of recorded traffic flows were used in the SDN network for flow feature extraction and real-time implementation. The results show very high and consistent performance metrics, with an average of 0.9 receiver-operating characteristics area under curve (ROC AUC) recorded. Furthermore, we compared the performance achieved using the original dataset with previous research to investigate the performance achieved using the same model developed. The flow-based intrusion model presented outperforms other publicly available methods, with a traffic anomaly detection rate of 98% accuracy and precision.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | autoencoder; deep learning; intrusion detection; network security; software-defined networking (SDN) |
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: | 21 Mar 2024 11:25 |
Last Modified: | 21 Mar 2024 11:25 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icc45855.2022.9838282 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210684 |