用大模型解析沙箱行为，提升恶意软件检测鲁棒性
Trident: Improving Malware Detection with LLMs and Behavioral Features
- 用大模型从少量样本生成基于行为的检测规则
- 新方法对概念漂移的抗性更强，误报率可控
- 融合静态特征、行为规则与大模型分析，效果优于单一方法
传统PE恶意软件检测依赖字节直方图、字符串信息和PE头内容等静态特征。动态分析报告因半结构化难以利用。我们发现，借助具备推理能力的最新大语言模型，可高效处理此类行为报告，并将其融入检测流程。具体而言，利用小规模标注样本，通过大模型生成基于行为特征的检测规则。这些规则对概念漂移具有更强鲁棒性，同时保持合理误报率。最后，我们提出Trident系统，结合经典决策树（静态特征）、行为规则及大模型对沙箱报告的直接分析，通过多数投票机制实现检测。Trident在性能上超越仅使用静态特征的标准方法，也优于仅依赖行为规则的方法，且在应对概念漂移时表现堪比主动学习，无需重新训练。
原文摘要 · Abstract (English)
Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE header contents. One barrier to incorporating dynamic analysis features has been the semi-structured nature of sandbox behavior reports. We show that, using the latest generation of large language models with reasoning, it is possible to efficiently process these behavior reports and utilize them as part of a malware detection pipeline. Specifically, we leverage LLMs to generate behavior-based malware detection rules based on a small training set of labeled malware. We find that these detection rules, derived from behavioral features, are much more robust to concept drift than standard static-feature methods, while maintaining practical false positive rates. Finally, we introduce Trident, a system which combines a classic decision tree model over static features, our behavior-based detection rules, and direct LLM analysis of sandbox reports through majority voting. Trident outperforms standard methods using static features, outperforms behavior-based rules alone, and is as resilient to concept drift as active learning methods without requiring retraining.
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