{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:18:13Z","timestamp":1775913493003,"version":"3.50.1"},"reference-count":72,"publisher":"Association for Computing Machinery (ACM)","issue":"8","funder":[{"name":"Macao Science and Technology Development","award":["0161\/2023\/RIA3"],"award-info":[{"award-number":["0161\/2023\/RIA3"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n            With the rise of large language models, such as ChatGPT, non-decisional models have been applied to various tasks. Moreover, ChatGPT has drawn attention to the traditional decision-centric task of Android malware detection. Despite effective detection methods proposed by scholars, they face low interpretability issues. Specifically, while these methods excel in classifying applications as benign or malicious and can detect malicious behavior, they often fail to provide detailed explanations for the decisions they make. This challenge raises concerns about the reliability of existing detection schemes and questions their true ability to understand complex data. In this study, we investigate the influence of the non-decisional model, ChatGPT, on the traditional decision-centric task of Android malware detection. We choose three state-of-the-art solutions,\n            <jats:italic toggle=\"yes\">Drebin<\/jats:italic>\n            ,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(XM_{AL}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , and\n            <jats:italic toggle=\"yes\">MaMaDroid<\/jats:italic>\n            , conduct a series of experiments on publicly available datasets, and carry out a comprehensive comparison and analysis. Our findings indicate that these decision-driven solutions primarily rely on statistical patterns within datasets to make decisions, rather than genuinely understanding the underlying data. In contrast, ChatGPT, as a non-decisional model, excels in providing comprehensive analysis reports, substantially enhancing interpretability. Furthermore, we conduct surveys among experienced developers. The result highlights developers\u2019 preference for ChatGPT, as it offers in-depth insights and enhances efficiency and understanding of challenges. Meanwhile, these studies and analyses offer profound insights, presenting developers with a novel perspective on Android malware detection\u2014enhancing the reliability of detection results from a non-decisional perspective.\n          <\/jats:p>","DOI":"10.1145\/3720541","type":"journal-article","created":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T10:53:26Z","timestamp":1740653606000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Android Malware Detection: The Influence of ChatGPT on Decision-centric Task"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0474-0159","authenticated-orcid":false,"given":"Yao","family":"Li","sequence":"first","affiliation":[{"name":"Macau University of Science and Technology, Taipa, Macao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9918-7180","authenticated-orcid":false,"given":"Sen","family":"Fang","sequence":"additional","affiliation":[{"name":"North Carolina State University at Raleigh, Raleigh, North Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6272-4069","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Macau University of Science and Technology, Taipa, Macao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5224-9970","authenticated-orcid":false,"given":"Haipeng","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of CSE, University at Buffalo, Buffalo, New York, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MALWARE.2015.7413692"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.002"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2017.11.006"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2014.23247"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2382196.2382222"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/sec.1723"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05195-w"},{"key":"e_1_3_2_9_2","unstructured":"Kathrin Beckert-Plewka Hauke Gierow Vera Haake and Stefan Karpenstein. 2020. 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