MalVIS: A pyramid vision transformer V2 (PVTv2) based framework for android malware detection
Devnath, Manoneet Mahesh Sikhwal, Basant Subba · 2025
This paper proposes MalVIS, an android malware detection framework based on pyramid vision transformers V2(PVTv2). MalVIS leverages the hierarchical self-attention mechanism of (PVTv2) to enhance detection of android based malware binaries. The pyramid structure of MalVIS enables it to efficiently capture the fine-grained and high-level features. It employs Spatial Reduction Attention (SRA) to reduce computational complexity by decreasing the number of tokens at each stage, which makes it suitable for deployment on resource-constrained environments. Additionally, MalVIS benefits from convolutional feed-forward networks (FFNs) to improve feature representation for effective malware classification. Experimental analysis on the benchmark MALNET-IMAGE dataset shows that MalVIS outperforms many state-of-the-art android malware detection frameworks, such as ViT-B Sherlock, ResNet, DenseNet, and MobileNetV2. It achieves an F1-score of 0.965 (binary classification) and 0.724 (multi-class classification) on the MALNET-IMAGE dataset.