Mitigating Malicious Adversaries Evasion Attacks in Industrial Internet of Things

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 1; pp. 960 - 968
• Main Authors: Rafiq, Husnain, Aslam, Nauman, Ahmed, Usman, Lin, Jerry Chun-Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial attacks; Analytical models; Android; Applications programs; Classification; Electronic devices; Feature extraction; Industrial applications; Industrial Internet of Things; Industrial Internet of Things (IIoT); Information retrieval; Internet of Things; Malware; Mobile computing; Object recognition; Smart phones; Static analysis
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3189046

With advanced 5G/6G 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 is vulnerable to attacks. A fabricated feature can force misclassification to produce the desired output. This article 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.