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Data provenance-based methods are widely used for APT detection but often rely on specific rules and high-quality data due to limitations in capturing complete graph structures, reducing their effectiveness in diverse detection environments. To overcome this issue, we propose APT-HERA, a model employs heterogeneous graph representation learning to learn system behavior patterns that can adapt to environments with limited data. The embedding representations of the provenance graph in APT-HERA are derived from both homophily and heterogeneity perspectives, thereby enabling a more comprehensive extraction of the rich structural information contained within the provenance graph. The performance of APT-HERA was evaluated on four public datasets. Experimental results demonstrate that APT-HERA achieves 98% precision in information-constrained detection scenarios, outperforming state-of-the-art methods including MAGIC, Flash, and ThreaTrace under such conditions.<\/jats:p>","DOI":"10.1186\/s42400-025-00425-x","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T03:01:43Z","timestamp":1772679703000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting advanced persistent threats via heterogeneous graph learning from homophily and heterogeneity views"],"prefix":"10.1186","volume":"9","author":[{"given":"Yuanhuang","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2606-5406","authenticated-orcid":false,"given":"Ayong","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Wenting","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Longjing","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"425_CR1","unstructured":"Alsaheel A, Nan Y, Ma S, Yu L, Walkup G, Celik ZB, Zhang X, Xu D (2021) ATLAS: a sequence-based learning approach for attack investigation. 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