Accelerating Malware Classification: A Vision Transformer Solution

The escalating frequency and scale of recent malware attacks underscore the urgentneed for swift and precise malware classification in the ever-evolving cybersecuritylandscape. Key challenges include accurately categorizing closely related malwarefamilies. To tackle this evolving threat landscape, this paper proposes a novelarchitecture LeViT-MC which produces state-of-the-art results in malware detectionand classification. LeViT-MC leverages a vision transformer-based architecture,an image-based visualization approach, and advanced transfer learning techniques.Experimental results on multi-class malware classification using the MaleVisdataset indicate LeViT-MC’s significant advantage over existing models. Thisstudy underscores the critical importance of combining image-based and transferlearning techniques, with vision transformers at the forefront of the ongoing battleagainst evolving cyber threats. We propose a novel architecture LeViT-MC whichnot only achieves state of the art results on image classification but is also moretime efficient.