# Accelerating Malware Classification: A Vision Transformer Solution

arXiv: https://arxiv.org/abs/2409.19461 · HTML: https://mlsanity.com/paper/2409.19461/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Benchmark Analysis of Representative Deep Neural Network Architectures](https://mlsanity.com/paper/1810.00736/) (2018)
- [MalBERT: Using Transformers for Cybersecurity and Malicious Software Detection](https://mlsanity.com/paper/2103.03806/) (2021)
- [Self-Supervised Vision Transformers for Malware Detection](https://mlsanity.com/paper/2208.07049/) (2022)
- [Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification](https://mlsanity.com/paper/1801.00318/) (2017)

## Built on by (most cited first, 2 of 2)

- [ViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection](https://mlsanity.com/paper/2606.12949/) (2026)
- [ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification](https://mlsanity.com/paper/2607.03653/) (2026)

## Abstract (arXiv)

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.