Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection

Dinh, PV; Quang Uy, N; Nguyen, DN; Thai Hoang, D; Bao, SP; Dutkiewicz, E

Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection

Dinh, PV Quang Uy, N Nguyen, DN Thai Hoang, D Bao, SP Dutkiewicz, E

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE Wireless Communications and Networking Conference, WCNC, 2022, 2022-April, pp. 848-853
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Dinh, PV
dc.contributor.author	Quang Uy, N
dc.contributor.author	Nguyen, DN
dc.contributor.author	Thai Hoang, D
dc.contributor.author	Bao, SP
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date	2022-04-10
dc.date.accessioned	2022-08-09T02:22:29Z
dc.date.available	2022-08-09T02:22:29Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Wireless Communications and Networking Conference, WCNC, 2022, 2022-April, pp. 848-853
dc.identifier.isbn	9781665442664
dc.identifier.issn	1525-3511
dc.identifier.uri	http://hdl.handle.net/10453/159795
dc.description.abstract	Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DE210100651
dc.relation.ispartof	IEEE Wireless Communications and Networking Conference, WCNC
dc.relation.ispartof	2022 IEEE Wireless Communications and Networking Conference (WCNC)
dc.relation.isbasedon	10.1109/WCNC51071.2022.9771793
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Twin Variational Auto-Encoder for Representation Learning in IoT Intrusion Detection
dc.type	Conference Proceeding
utslib.citation.volume	2022-April
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-01-01T00:00:00+1000Z
dc.date.updated	2022-08-09T02:22:28Z
pubs.finish-date	2022-04-13
pubs.publication-status	Published
pubs.start-date	2022-04-10
pubs.volume	2022-April

Abstract:

Intrusion detection systems (IDSs) play a pivotal role in defending IoT systems. However, developing a robust and efficient IDS is challenging due to the rapid and continuing evolving of various forms of cyber-attacks as well as a massive number of low-end IoT devices. In this paper, we introduce a novel deep learning architecture based on auto-encoders that allows to develop a robust intrusion detection system. Specifically, we propose a novel neural network architecture called Twin Variational Auto-Encoder (TVAE) for representation learning. TVAE includes a variational Auto-Encoder (VAE) and an Auto-Encoder (AE) that share a common stage where the decoder of the VAE is used as the encoder of the AE. The TVAE is trained in an unsupervised manner to effectively transform the original representation of data at the input of the VAE into a new representation at the output of the AE. In the new representation space, the difference between normal and attack data is more distinguishable. A variant of TVAE, namely Twin Sparse Variational Auto-Encoder (TSVAE) is also introduced by imposing a sparsity constraint on the representation units. The effectiveness of TVAE and TSVAE is evaluated using popular IDS and IoT botnet datasets. The simulation results show that the accuracy of TVAE and TSVAE can achieve the best results on six datasets, which is higher than those of state-of-the-art AE and VAE variants. We also investigate various characteristics of TVAE in the latent space as well as in the data extraction process. Besides applications on the IoT IDS, TVAE can also be applicable to all conventional network IDSs.

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http://hdl.handle.net/10453/159795