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arXiv · 2512.15039

APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication

Zhenhao Yin·Hanbing Yan·Huishu Lu·Jing Xiong·Xiangyu Li·Rui Mei·Tianning Zang
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Abstract

Large-scale, standardized datasets for Advanced Persistent Threat (APT) research are scarce, and inconsistent actor aliases and redundant samples hinder reproducibility. This paper presents APT-ClaritySet and its construction pipeline that normalizes threat actor aliases (reconciling approximately 11.22\% of inconsistent names) and applies graph-feature deduplication -- reducing the subset of statically analyzable executables by 47.55\% while retaining behaviorally distinct variants. APT-ClaritySet comprises: (i) APT-ClaritySet-Full, the complete pre-deduplication collection with 34{,}363 malware samples attributed to 305 APT groups (2006 - early 2025); (ii) APT-ClaritySet-Unique, the deduplicated release with 25{,}923 unique samples spanning 303 groups and standardized attributions; and (iii) APT-ClaritySet-FuncReuse, a function-level resource that includes 324{,}538 function-reuse clusters (FRCs) enabling measurement of inter-/intra-group sharing, evolution, and tooling lineage. By releasing these components and detailing the alias normalization and scalable deduplication pipeline, this work provides a high-fidelity, reproducible foundation for quantitative studies of APT patterns, evolution, and attribution.

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Zhenhao Yin, Hanbing Yan, Huishu Lu, Jing Xiong, Xiangyu Li, Rui Mei, Tianning Zang. 2025-12-17. APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication. https://arxiv.org/abs/2512.15039

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