构建首个超大规模分层图数据集，助力恶意软件分析。
HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
- 构建两级分层图：59.5万函数调用图嵌套2亿控制流图。
- 揭示良性和恶意软件在结构上的显著差异。
- 适合恶意软件检测、图神经网络研究者使用。
图式恶意软件分析的发展严重受限于缺乏能捕捉软件固有层次结构的大规模数据集。现有方法常将程序简化为单层图，无法建模高层功能交互与底层指令逻辑间的语义关系。为此，我们提出HiGraph，目前公开领域最大的分层图数据集，包含超过2亿个控制流图（CFGs）嵌套于59.5万个函数调用图（FCGs）中。该两级表示保留了构建鲁棒检测器所需的关键结构语义，可抵御代码混淆和恶意软件演化。我们通过大规模分析验证了其价值，揭示了良性与恶意软件在结构上的显著差异，确立了其作为社区基准的地位。数据集及工具已公开：https://higraph.org。
原文摘要 · Abstract (English)
The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing methods often oversimplify programs into single level graphs, failing to model the crucial semantic relationship between high-level functional interactions and low-level instruction logic. To bridge this gap, we introduce \dataset, the largest public hierarchical graph dataset for malware analysis, comprising over \textbf{200M} Control Flow Graphs (CFGs) nested within \textbf{595K} Function Call Graphs (FCGs). This two-level representation preserves structural semantics essential for building robust detectors resilient to code obfuscation and malware evolution. We demonstrate HiGraph's utility through a large-scale analysis that reveals distinct structural properties of benign and malicious software, establishing it as a foundational benchmark for the community. The dataset and tools are publicly available at https://higraph.org.
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