Accelerating Malware Classification: A Vision Transformer Solution
<|reference_start|>Accelerating Malware Classification: A Vision Transformer Solution: The escalating frequency and scale of recent malware attacks underscore the urgent need for swift and precise malware classification in the ever-evolving cybersecurity landscape. Key challenges include accurately categorizing closely related malware families. To tackle this evolving threat landscape, this paper proposes a novel architecture LeViT-MC which produces state-of-the-art results in malware detection and 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 MaleVis dataset indicate LeViT-MC's significant advantage over existing models. This study underscores the critical importance of combining image-based and transfer learning techniques, with vision transformers at the forefront of the ongoing battle against evolving cyber threats. We propose a novel architecture LeViT-MC which not only achieves state of the art results on image classification but is also more time efficient.<|reference_end|>
@article{bavishi2024accelerating,
    title={Accelerating Malware Classification: A Vision Transformer Solution},
    author={Shrey Bavishi, Shrey Modi},
    journal={arXiv preprint arXiv:2409.19461},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19461},
    primaryClass={cs.CR cs.CV cs.LG}
}