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.

中文翻译：

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减轻工业物联网中的恶意对手规避攻击


随着先进的5G/6G网络的发展，数据驱动的互联设备将呈指数级增长。因此，工业物联网 (IIoT) 需要数据安全信息提取来应用数字服务、医疗诊断和财务预测。这也将适应高速网络移动应用的推出。因此，Android 恶意软件的规模和复杂性不断上升。恶意软件分类的检测容易受到攻击。伪造的特征可能会强制错误分类以产生所需的输出。本文提出了一种子集特征选择方法来规避 IIoT 环境中的伪造攻击。该方法从单个 Android 应用程序中提取应用程序感知特征来训练独立的分类模型。然后使用基于集成的学习来训练不同的分类模型。最后，协作机器学习分类器做出独立决策来对抗对抗性逃避攻击。我们比较和评估基准 Android 恶意软件数据集。所提出的方法通过 14 个虚构的输入特征实现了 91% 的准确率。