Ahmed, Imran 
ORCID: https://orcid.org/0000-0002-7751-286X, Anisetti, Marco, Ahmad, Awais and Jeon, Gwanggil
(2022)
A Multi-layer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT.
IEEE Transactions on Industrial Informatics.
pp. 1-9.
ISSN 1941-0050
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Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
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Accepted Version Available under the following license: Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
5 G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective low-latency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system, and introduce new powerful attacks vectors leading to severe security and data privacy risks. Malware attacks are starting targeting weak but highly connected IoT devices showing the importance of security and privacy in this scenario. This paper designs a 5G-enabled system, consisted in a deep learning-based architecture aimed to classify malware attacks on the IIoT. Our methodology is based on an image representation of the malware and a Convolutional Neural Networks (CNNs) that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy.
| Item Type: | Journal Article |
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| Keywords: | 5G, Cybersecurity, Industrial IoT, Malware detection |
| Faculty: | Faculty of Science & Engineering |
| SWORD Depositor: | Symplectic User |
| Depositing User: | Symplectic User |
| Date Deposited: | 13 Sep 2022 10:47 |
| Last Modified: | 13 Sep 2022 10:47 |
| URI: | https://arro.anglia.ac.uk/id/eprint/707904 |
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