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A Hybrid Deep Learning Approach for Intrusion Detection in IoT Networks

EMEC, M. See more information about EMEC, M. on SCOPUS See more information about EMEC, M. on IEEExplore See more information about EMEC, M. on Web of Science, OZCANHAN, M. H. See more information about OZCANHAN, M. H. on SCOPUS See more information about OZCANHAN, M. H. on SCOPUS See more information about OZCANHAN, M. H. on Web of Science
 
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Download PDF pdficon (1,449 KB) | Citation | Downloads: 728 | Views: 553

Author keywords
hybrid intelligent systems, Internet of Things, intrusion detection, learning systems, prediction methods

References keywords
learning(19), detection(17), deep(15), network(14), intrusion(13), link(7), system(6), security(6), neural(6), model(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01001
Web of Science Accession Number: 000762769600002
SCOPUS ID: 85126801506

Abstract
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Internet of Things (IoT) devices have flocked the whole world through the Internet. With increasing mission-critical IoT data traffic, attacks on IoT networks have also increased. Many newly crafted attacks on IoT communication require equally intelligent intrusion detection methods to form the first step of countering the attacks. Our work contributes to intrusion detection in IoT networks, by putting state-of-the-art Deep learning methods into service. A BLSTM-GRU Hybrid (BGH) model has been designed to detect eight known IoT network attacks, based on two well-accepted CIC-IDS-2018 and BoT-IoT IoT network traffic datasets. The results of our BGH model in IoT network traffic intrusion detection have been auspicious. The accuracies of prediction on the two datasets are 98.78% and 99.99%. The f1-scores are 98.64% and 99.99%, respectively. The comparison of our results with similar previous studies showed that our BGH model has the best performance ratio (time/accuracy, time/f1-score), where time is the training time of the model. The performance of our proposed model is proof that hybrid Deep Learning methods can prove to be an innovative perspective on Intrusion Detection in IoT networks.


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References Weight

Web of Science® Citations for all references: 27,984 TCR
SCOPUS® Citations for all references: 46,593 TCR

Web of Science® Average Citations per reference: 622 ACR
SCOPUS® Average Citations per reference: 1,035 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-07-01 12:10 in 235 seconds.




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