{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T04:26:39Z","timestamp":1728447999446},"reference-count":80,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"name":"National Key Projects of Research and Development","award":["2022YFC3301305, 2023YFC3304400"]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["62172100, 92370120"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"Recently, there is a surge in ransomware activities that encrypt users\u2019 sensitive data and demand bitcoins for ransom payments to conceal the criminal\u2019s identity. It is crucial for regulatory agencies to identify as many ransomware addresses as possible to accurately estimate the impact of these ransomware activities. However, existing methods for detecting ransomware addresses rely primarily on time-consuming data collection and clustering heuristics, and they face two major issues: (1) The features of an address itself are insufficient to accurately represent its activity characteristics, and (2) the number of disclosed ransomware addresses is extremely less than the number of unlabeled addresses. These issues lead to a significant number of ransomware addresses being undetected, resulting in a substantial underestimation of the impact of ransomware activities.<\/jats:p>\n \n To solve the above two issues, we propose an optimized ransomware address detection method based on Bitcoin transaction relationships, named\n XRAD<\/jats:monospace>\n , to detect more ransomware addresses with high performance. To address the first one, we present a cascade feature extraction method for Bitcoin transactions to aggregate features of related addresses after exploring transaction relationships. To address the second one, we build a classification model based on Positive-unlabeled learning to detect ransomware addresses with high performance. Extensive experiments demonstrate that\n XRAD<\/jats:monospace>\n significantly improves average accuracy, recall, and F1 score by 15.07%, 19.71%, and 34.83%, respectively, compared to state-of-the-art methods. In total,\n XRAD<\/jats:monospace>\n detects 120,335 ransomware activities from 2009 to 2023, revealing a development trend and average ransom payment per year that aligns with three reports by FinCEN, Chainalysis, and Coveware.\n <\/jats:p>","DOI":"10.1145\/3687487","type":"journal-article","created":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T11:30:21Z","timestamp":1724153421000},"page":"1-33","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["XRAD: Ransomware Address Detection Method based on Bitcoin Transaction Relationships"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-3001-2665","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1939-5930","authenticated-orcid":false,"given":"Michael","family":"Tong","sequence":"additional","affiliation":[{"name":"software school, Fudan University, Shanghai, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4521-4112","authenticated-orcid":false,"given":"Jun","family":"Pang","sequence":"additional","affiliation":[{"name":"Computer Science and Communications, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6842-2793","authenticated-orcid":false,"given":"Jitao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8663-436X","authenticated-orcid":false,"given":"Weili","family":"Han","sequence":"additional","affiliation":[{"name":"Software School, Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"4439","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI\u201920)","author":"Akcora Cuneyt Gurcan","year":"2020","unstructured":"Cuneyt Gurcan Akcora, Yitao Li, Yulia R. 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