用视觉Transformer提升恶意软件分类速度与准确率
Accelerating Malware Classification: A Vision Transformer Solution
- 将恶意软件转化为图像，用ViT架构分析
- 在MaleVis数据集上达到顶尖分类效果
- 适合需要快速响应的网络安全场景
近年来恶意软件攻击频发且规模扩大，亟需高效精准的分类方法。现有挑战在于准确区分相似恶意软件家族。本文提出新型架构LeViT-MC，结合基于图像的可视化方法、视觉变压器架构与先进迁移学习技术，在MaleVis数据集上的多类恶意软件分类实验中显著优于现有模型。结果表明，图像表示与迁移学习的融合对应对不断演化的网络威胁至关重要，该方法不仅在图像分类上达到前沿水平，还具备更优的时间效率。
原文摘要 · Abstract (English)
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.
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