Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things

Rafiq, Husnain, Aslam, Nauman, Ahmed, Usman and Lin, Jerry Chun-Wei (2022) Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things. IEEE Transactions on Industrial Informatics. pp. 1-9. ISSN 1551-3203 (In Press)

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Official URL: https://doi.org/10.1109/TII.2022.3189046

Abstract

With advanced 5 G/6 G networks, data-driven interconnected devices will increase exponentially. As a result, the Industrial Internet of Things (IIoT) requires data secure information extraction to apply digital services, medical diagnoses and financial forecasting. This introduction of high-speed network mobile applications will also adapt. As a consequence, the scale and complexity of Android malware are rising. Detection of malware classification vulnerable to attacks. A fabricate feature can force misclassification to produce the desired output. This study proposes a subset feature selection method to evade fabricated attacks in the IIOT environment. The method extracts application-aware features from a single android application to train an independent classification model. Ensemble-based learning is then used to train the distinct classification models. Finally, the collaborative ML classifier makes independent decisions to fight against adversarial evasion attacks. We compare and evaluate the benchmark Android malware dataset. The proposed method achieved 91% accuracy with 14 fabricated input features.

Item Type: Article
Additional Information: Funding information: This work is partially supported by the National Centre for Research and Development under the project Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective and the Project Contract no. is: NOR/POLNOR/CoBotAGV/0027/2019 -00.
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 29 Jul 2022 14:03
Last Modified: 29 Jul 2022 14:42
URI: http://nrl.northumbria.ac.uk/id/eprint/49657

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