Secure IIoT-enabled industry 4.0
The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent thre...
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| Format: | Article |
| Language: | en en |
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MDPI
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/33421/1/Secure%20IIoT-enabled%20industry%204.0.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/33421/2/Secure%20IIoT-enabled%20industry%204.0.pdf https://eprints.ums.edu.my/id/eprint/33421/ https://www.mdpi.com/2071-1050/13/22/12384 https://doi.org/10.3390/su132212384 |
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| _version_ | 1831795094206808064 |
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| author | Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani |
| author_facet | Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani |
| author_sort | Zeeshan Hussain |
| building | UMS Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sabah |
| content_source | UMS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance |
| format | Article |
| id | my.ums.eprints-33421 |
| institution | Universiti Malaysia Sabah |
| language | en en |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | eprints |
| spelling | my.ums.eprints-334212022-07-21T01:20:50Z https://eprints.ums.edu.my/id/eprint/33421/ Secure IIoT-enabled industry 4.0 Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani QA76.75-76.765 Computer software T1-995 Technology (General) The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performance MDPI 2021-11-10 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/33421/1/Secure%20IIoT-enabled%20industry%204.0.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/33421/2/Secure%20IIoT-enabled%20industry%204.0.pdf Zeeshan Hussain and Adnan Akhunzada and Javed Iqbal and Iram Bibi and Abdullah Gani (2021) Secure IIoT-enabled industry 4.0. Sustainability, 13. pp. 1-14. ISSN 2071-1050 https://www.mdpi.com/2071-1050/13/22/12384 https://doi.org/10.3390/su132212384 https://doi.org/10.3390/su132212384 |
| spellingShingle | QA76.75-76.765 Computer software T1-995 Technology (General) Zeeshan Hussain Adnan Akhunzada Javed Iqbal Iram Bibi Abdullah Gani Secure IIoT-enabled industry 4.0 |
| title | Secure IIoT-enabled industry 4.0 |
| title_full | Secure IIoT-enabled industry 4.0 |
| title_fullStr | Secure IIoT-enabled industry 4.0 |
| title_full_unstemmed | Secure IIoT-enabled industry 4.0 |
| title_short | Secure IIoT-enabled industry 4.0 |
| title_sort | secure iiot-enabled industry 4.0 |
| topic | QA76.75-76.765 Computer software T1-995 Technology (General) |
| url | https://eprints.ums.edu.my/id/eprint/33421/1/Secure%20IIoT-enabled%20industry%204.0.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/33421/2/Secure%20IIoT-enabled%20industry%204.0.pdf https://eprints.ums.edu.my/id/eprint/33421/ https://www.mdpi.com/2071-1050/13/22/12384 https://doi.org/10.3390/su132212384 https://doi.org/10.3390/su132212384 |
| url_provider | http://eprints.ums.edu.my/ |