Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0

Gupta, BB; Tewari, A; Cvitić, I; Peraković, D; Chang, X

Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0

Gupta, BB Tewari, A Cvitić, I Peraković, D Chang, X

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Wireless Networks, 2022, 28, (1), pp. 493-503
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Gupta, BB
dc.contributor.author	Tewari, A
dc.contributor.author	Cvitić, I
dc.contributor.author	Peraković, D
dc.contributor.author	Chang, X https://orcid.org/0000-0002-7778-8807
dc.date.accessioned	2023-03-20T02:51:42Z
dc.date.available	2023-03-20T02:51:42Z
dc.date.issued	2022-01-01
dc.identifier.citation	Wireless Networks, 2022, 28, (1), pp. 493-503
dc.identifier.issn	1022-0038
dc.identifier.issn	1572-8196
dc.identifier.uri	http://hdl.handle.net/10453/167718
dc.description.abstract	In this paper, we introduce an approach to secure IoT devices from unsolicited emails by using certain AI-based features and clustering in real-time. We propose a novel approach that first filters the unwanted emails from the incoming traffic and then classifies them into spam and phishing for Internet of Things (IoTs) based systems in industry 4.0. The AI mechanism collects and analyzes emails to detect multiple features that identify patterns for classification. We divided our incoming data into batches and each batch was classified based on knowledge gained from previous batch's classification. We tested our results with a number of classifiers and results show that our approach gives highly accurate classification.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Wireless Networks
dc.relation.isbasedon	10.1007/s11276-021-02619-w
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0
dc.type	Journal Article
utslib.citation.volume	28
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-20T02:51:41Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	1

Abstract:

In this paper, we introduce an approach to secure IoT devices from unsolicited emails by using certain AI-based features and clustering in real-time. We propose a novel approach that first filters the unwanted emails from the incoming traffic and then classifies them into spam and phishing for Internet of Things (IoTs) based systems in industry 4.0. The AI mechanism collects and analyzes emails to detect multiple features that identify patterns for classification. We divided our incoming data into batches and each batch was classified based on knowledge gained from previous batch's classification. We tested our results with a number of classifiers and results show that our approach gives highly accurate classification.

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http://hdl.handle.net/10453/167718