MalVIS: A pyramid vision transformer V2 (PVTv2) based framework for android malware detection

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.