5G 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 introduces new powerful attack 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 article 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 that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy

A multi-layer deep learning approach for malware classification in 5G-enabled IIoT / I. Ahmed, M. Anisetti, A. Ahmad, G. Jeon. - In: IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. - ISSN 1551-3203. - 19:2(2023 Feb), pp. 1495-1503. [10.1109/TII.2022.3205366]

A multi-layer deep learning approach for malware classification in 5G-enabled IIoT

M. Anisetti
Secondo
;
2023

Abstract

5G 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 introduces new powerful attack 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 article 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 that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy
5G; cybersecurity; deep learning; Industrial Internet of Things (IoT); malware detection
Settore INF/01 - Informatica
H20_RIA19EDAMI_01 - Cyber security cOmpeteNce fOr Research anD Innovation (CONCORDIA) - DAMIANI, ERNESTO - H20_RIA - Horizon 2020_Research & Innovation Action/Innovation Action - 2019
PSRL222SCAST_01 - Piano di Sostegno alla Ricerca 2015-2017 - Linea 2 "Dotazione annuale per attività istituzionali" (anno 2021) - CASTANO, SILVANA - PSR_LINEA2_ / Piano di sviluppo di ricerca - Dotazioni dipartimentali - Linea 2 - 2022
9-set-2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/947528
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