HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916045010239488 |
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| author | Chen, Han Wang, Hanchen Chen, Hongmei Zhang, Ying Qin, Lu Zhang, Wenjie |
| author_facet | Chen, Han Wang, Hanchen Chen, Hongmei Zhang, Ying Qin, Lu Zhang, Wenjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis Chen, Han Wang, Hanchen Chen, Hongmei Zhang, Ying Qin, Lu Zhang, Wenjie Machine Learning Artificial Intelligence Cryptography and Security Social and Information Networks 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. |
| title | HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Social and Information Networks |
| url | https://arxiv.org/abs/2509.02113 |