A lightweight hybrid CNN–vision transformer framework for image-based Android malware detection

The rapid development of android applications has been accompanied by a consistent outpouring in sophisticated malware variations that progressively elude signature-based and traditional machine learning detection systems. While considering deep learning models like convolutional and recurrent neural networks, improved results are attained but they rely on local feature extraction and lack in modelling long-range dependencies which limit their adaptability towards advanced obfuscated and polymorphic malwares. In this research work, a lightweight hybrid malware detection framework is proposed which combines MobileNetV2 and a Vision Transformer (ViT) to empower effective and strong Android malware classification from image-based depictions of application binaries. Compact spatial features are extracted by MobileNetV2 and global contextual relationships are captured using self-attention mechanisms by Vision Transformer. Thus, this hybrid design attains effective balance between computational efficiency and detection accuracy. By leveraging the large-scale MalNet dataset comprised of 8 android malware families, the proposed model is evaluated and experimental results presents 98% accuracy, precision, recall, and F1-score by outperforming recent proposed methods based on CNN, transformer, and hybrid baselines. It proves our proposed model maintains compact model size and low inference latency. Further, the results demonstrate that this integration will provide appropriate deployment solution for android malware detection especially real-time security operations which are resource constrained.