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Rezvy, Shahadate, Luo, Yuan, Petridis, Miltos, Lasebae, Aboubaker and Zebin, Tahmina 
ORCID: https://orcid.org/0000-0003-0437-0570
(2019)
An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks.
In:
2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019.
The Institute of Electrical and Electronics Engineers (IEEE), USA.
ISBN 9781728111513
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Abstract
A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | autoencoder,computer network security,deep learning,dense neural network,intrusion detection system,information systems ,/dk/atira/pure/subjectarea/asjc/1700/1710 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 28 May 2019 13:30 |
| Last Modified: | 05 May 2024 03:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/71146 |
| DOI: | 10.1109/CISS.2019.8693059 |
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