EMTD-SSC: An Enhanced Malicious Traffic Detection Model Using Transfer Learning Under Small Sample Conditions In IoT

Ge, Y; Gao, Y; Li, X; Cai, B; Xi, J; Yu, S

EMTD-SSC: An Enhanced Malicious Traffic Detection Model Using Transfer Learning Under Small Sample Conditions In IoT

Ge, Y Gao, Y Li, X Cai, B Xi, J Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Ge, Y
dc.contributor.author	Gao, Y
dc.contributor.author	Li, X
dc.contributor.author	Cai, B
dc.contributor.author	Xi, J
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-09-05T10:49:58Z
dc.date.available	2024-09-05T10:49:58Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Internet of Things Journal, 2024, PP, (99), pp. 1-1
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180682
dc.description.abstract	In the Internet of Things (IoT) scenario, device diversity and data sparsity present a significant challenge for malicious traffic detection, notably the ‘small sample problem’ where insufficient data hampers the performance of deep learning methods that depend on large volumes of labeled data for training. Transfer learning has the capability to transfer knowledge from a label-rich but heterogeneous domain to a label-sparse domain, making it a powerful tool for addressing challenges in IoT malicious traffic detection. To address these challenges, we introduce the EMTD-SSC model, a novel Enhanced Malicious Traffic Detection model that leverages transfer learning under Small Sample Conditions in IoT environments. Initially, our approach includes a comprehensive labeled dataset that merges a small-scale IoT intrusion detection domain with the traditional intrusion detection domain to enrich semantic information transfer from source to target domains. The EMTD-SSC model employs dual residual convolutional autoencoders for robust feature extraction and transfer, incorporating skip connections to expedite model convergence and minimize information loss. Furthermore, to optimize transfer efficiency, we minimize the Multi Layer Multi Kernel Maximum Mean Discrepancy (MLMK-MMD) across corresponding network layers, facilitating effective domain adaptation. Through unsupervised training and subsequent fine-tuning on target domain data, the model significantly enhances anomaly detection capabilities. Extensive experiments on two well-known public datasets demonstrate that the EMTD-SSC model’s effectiveness, achieving an impressive 94.8% accuracy in binary classification tasks.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2024.3413580
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	EMTD-SSC: An Enhanced Malicious Traffic Detection Model Using Transfer Learning Under Small Sample Conditions In IoT
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-09-05T10:49:55Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

In the Internet of Things (IoT) scenario, device diversity and data sparsity present a significant challenge for malicious traffic detection, notably the ‘small sample problem’ where insufficient data hampers the performance of deep learning methods that depend on large volumes of labeled data for training. Transfer learning has the capability to transfer knowledge from a label-rich but heterogeneous domain to a label-sparse domain, making it a powerful tool for addressing challenges in IoT malicious traffic detection. To address these challenges, we introduce the EMTD-SSC model, a novel Enhanced Malicious Traffic Detection model that leverages transfer learning under Small Sample Conditions in IoT environments. Initially, our approach includes a comprehensive labeled dataset that merges a small-scale IoT intrusion detection domain with the traditional intrusion detection domain to enrich semantic information transfer from source to target domains. The EMTD-SSC model employs dual residual convolutional autoencoders for robust feature extraction and transfer, incorporating skip connections to expedite model convergence and minimize information loss. Furthermore, to optimize transfer efficiency, we minimize the Multi Layer Multi Kernel Maximum Mean Discrepancy (MLMK-MMD) across corresponding network layers, facilitating effective domain adaptation. Through unsupervised training and subsequent fine-tuning on target domain data, the model significantly enhances anomaly detection capabilities. Extensive experiments on two well-known public datasets demonstrate that the EMTD-SSC model’s effectiveness, achieving an impressive 94.8% accuracy in binary classification tasks.

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http://hdl.handle.net/10453/180682