发现静态杀毒模型误把打包痕迹当恶意行为，导致判断不可靠
Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE
- 用TRUSTEE工具分析模型关注点，识别关键特征
- 顶级特征多为打包痕迹和PE元数据，非真实恶意语义
- 适合安全研究人员和模型开发者参考改进
现代网络安全严重依赖基于机器学习的静态恶意软件分类器。然而，打包等非语义变换会降低其可靠性。由于恶意性与打包高度相关，分类器常学习这些无关特征而非真实二进制行为。且分类器为黑箱，难以理解其决策依据。为此，我们提出两阶段框架：先使用后处理可解释性工具TRUSTEE识别重要特征，再进行人工分析。通过控制数据集组成比例开展多组实验，结果表明所有实验中排名最高的特征均为打包痕迹、可移植执行体(PE)元数据及字符串级n-grams，而非恶意语义。这说明分类器对数据集构成高度敏感，可能将打包误判为恶意行为。该框架可复现地诊断此类偏差，为构建更鲁棒、语义明确的恶意软件检测模型提供指导。
原文摘要 · Abstract (English)
Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-semantic modifications applied to executable files limit their reliability. Malware classifiers often learn these unnecessary artifacts rather than the true binary behavior because of the high association between maliciousness and packing. Moreover, these malware classifiers are black boxes, making it difficult to understand what they learn. To address this issue, we proposed a two-part framework using the post-hoc interpretability XAI tool TRUSTEE, followed by a manual analysis of the top features. We conducted several controlled experiments by varying the dataset composition ratios to understand their impact on the results. The top-ranked features across all experiments, identified by TRUSTEE, were predominantly packing artifacts, portable executable(PE) metadata, and n-grams at the string level, rather than malicious semantics. These results suggest that these malware classifiers are highly sensitive to dataset composition and can misinterpret packing as malicious behavior. Our proposed framework allows for the reproducible diagnosis of such biases and forms a guideline for building more robust and semantically meaningful malware detection models
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