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

Sidra Javed, Guowei Wu, Hamza Javed, Osama Ahmed Khashan, Haseeb Hassan, Anwar Ghani

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1016/j.array.2026.100681
Data confidence 70 medium
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Abstract

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.