An automated multi-scale and multi-contextual MobileNetv3 for malware detection based on IoT

Javed S. Wu G. Javed H. Khashan O.A. Hassan H. Ghani A.
March 2026 Elsevier B.V.

Array
2026 #29

Malware detection is a crucial aspect of cybersecurity, aimed at identifying and mitigating malicious software that poses threats to systems and networks. Traditional malware detection methods face challenges in terms of both detection accuracy and computational cost, as deep learning models can be resource-intensive and difficult to deploy in real-time environments. This paper introduces the novel MSMC-MobileNet (Multi-Scale and Multi-Contextual MobileNet) malware detection and classification model, designed to address the challenges of accuracy and computational cost. First, MobileNetv3 is used to extract features from the dataset. To enhance feature extraction, the SE (Squeeze-and-Excitation) module is integrated, focusing on the region of interest using an attention mechanism. The multiscale and multicontextual features are extracted using the ASPP (Atrous Spatial Pyramid Pooling) and FPP (Feature Pyramid Pooling) modules. Channel-wise pruning is applied to the ASPP and FPP modules, reducing computational cost. The model is evaluated on the publicly available Malimg and MaleVis datasets. The proposed MSMC-MobileNet model achieves impressive performance with 92.37% accuracy, 96.54% precision, 95.84% recall, 95.47% F1 score, and 98.59% AUC on the Malimg dataset. On the MaleVis dataset, the model yields 95.08% accuracy, 98.33% precision, 97.9% recall, 98.15% F1 score, and 96.98% AUC. When both datasets are combined, the MSMC-MobileNet achieves 98.79% accuracy, 99.84% precision, 99.73% recall, 99.89% F1 score, and 1.00 AUC. Despite its high accuracy, the model remains computationally efficient, outperforming state-of-the-art methods in both detection performance and computational cost.

Deep learning , IoT , Malware detection , MobileNetv3 , Multi-scale features

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School of Software Technology, Dalian University of Technology, Liaoning, Dalian, 116024, China
School of Computer Science and Engineering, Central South University, Hunan, Changsha, 410017, China
Research and Innovation Centers, Rabdan Academy, 114646, United Arab Emirates
School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan

School of Software Technology
School of Computer Science and Engineering
Research and Innovation Centers
School of Medicine
Department of Computer Science

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