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Conventional deep learning detection approaches struggle to identify new malware variants with limited sample availability. Recently, researchers have proposed few\u2010shot detection models to address the above issues. However, existing studies predominantly focus on model\u2010level improvements, overlooking the potential of domain adaptation to leverage the unique characteristics of malware. Motivated by these challenges, we propose a few\u2010shot learning\u2010based malware family detection framework (MalFSLDF). We introduce a novel method for malware representation using structural features and a feature fusion strategy. Specifically, our framework employs contrastive learning to capture the unique textural features of malware families, enhancing the identification capability for novel malware variants. In addition, we integrate entropy graphs (EGs) and gray\u2010level co\u2010occurrence matrices (GLCMs) into the feature fusion strategy to enrich sample representations and mitigate information loss. Furthermore, a domain alignment strategy is proposed to adjust the feature distribution of samples from new classes, enhancing the model\u2019s generalization performance. Finally, comprehensive evaluations of the MaleVis and BIG\u20102015 datasets show significant performance improvements in both 5\u2010way 1\u2010shot and 5\u2010way 5\u2010shot scenarios, demonstrating the effectiveness of the proposed framework.<\/jats:p>","DOI":"10.1155\/int\/7390905","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T06:36:56Z","timestamp":1745476616000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MalFSLDF: A Few\u2010Shot Learning\u2010Based Malware Family Detection Framework"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8544-4686","authenticated-orcid":false,"given":"Wenjie","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3087-9701","authenticated-orcid":false,"given":"Jingfeng","family":"Xue","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8716-0450","authenticated-orcid":false,"given":"Yuxin","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1551-7125","authenticated-orcid":false,"given":"Wenbiao","family":"Du","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3220-621X","authenticated-orcid":false,"given":"Jingjing","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6504-425X","authenticated-orcid":false,"given":"Ning","family":"Shi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3793-8222","authenticated-orcid":false,"given":"Weijie","family":"Han","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"e_1_2_12_1_2","unstructured":"AvTest Malware Statistics 2020 https:\/\/www.av-test.org\/en\/-statistics\/malware\/."},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2022.100529"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106030"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108374"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2016904.2016908"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2021.3119778"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2024.3350379"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122678"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2024.3433372"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3582688"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00353"},{"key":"e_1_2_12_13_2","first-page":"2672","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems","author":"Goodfellow I. 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