Scholarlink

<p>Malware family classification is crucial for threat detection, yet existing methods struggle with generalization, multi-task adaptability, and interpretability. We propose VIMAR, a unified vision–language model that supports classification, similarity detection, and open-world analysis via explanation-rich supervision and a two-stage training pipeline. On the Malimg dataset, VIMAR achieves 94.2% accuracy in family classification, surpassing the best CNN baseline by +3.1%. It also attains 85.2% and 88.0% accuracy in zero-shot and few-shot settings, significantly outperforming vision–language baselines. Moreover, its reasoning outputs align well with human judgments. The codebase and scripts will be released to the community.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

VIMAR: vision-language informed malware analysis and reasoning model

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

查看全文
  • Shiting Xu

摘要

Malware family classification is crucial for threat detection, yet existing methods struggle with generalization, multi-task adaptability, and interpretability. We propose VIMAR, a unified vision–language model that supports classification, similarity detection, and open-world analysis via explanation-rich supervision and a two-stage training pipeline. On the Malimg dataset, VIMAR achieves 94.2% accuracy in family classification, surpassing the best CNN baseline by +3.1%. It also attains 85.2% and 88.0% accuracy in zero-shot and few-shot settings, significantly outperforming vision–language baselines. Moreover, its reasoning outputs align well with human judgments. The codebase and scripts will be released to the community.