{"ID":2827837,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15039","arxiv_id":"2512.15039","title":"APT-ClaritySet: A Large-Scale, High-Fidelity Labeled Dataset for APT Malware with Alias Normalization and Graph-Based Deduplication","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.","short_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 inconsiste...","url_abs":"https://arxiv.org/abs/2512.15039","url_pdf":"https://arxiv.org/pdf/2512.15039v1","authors":"[\"Zhenhao Yin\",\"Hanbing Yan\",\"Huishu Lu\",\"Jing Xiong\",\"Xiangyu Li\",\"Rui Mei\",\"Tianning Zang\"]","published":"2025-12-17T03:09:08Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.SE\"]","methods":"[]","has_code":false}