{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T16:53:56Z","timestamp":1787763236651,"version":"build-2784847793"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,12,9]],"date-time":"2023-12-09T00:00:00Z","timestamp":1702080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Emerging Areas Research Projects (EARP) Funding Initiative"},{"name":"Industry Alignment Fund\u2013Pre-positioning (IAF-PP) Funding Initiative"},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["72171071, 72271084, 72101079"],"award-info":[{"award-number":["72171071, 72271084, 72101079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Excellent Fund of HFUT","award":["JZ2021HGPA0060"],"award-info":[{"award-number":["JZ2021HGPA0060"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain address are much scarcer than other cryptocurrency platforms.<\/jats:p>\n          <jats:p>We first define asset transfer paths with the Decision Tree based feature Selection and Complement to build different feature sets for different malice types. Then, the Status\/Action Proposal module and the Intention-VAE module generate the status, action, intent-snippet, and hidden intent-snippet embedding. With all these modules, our model is highly interpretable and can detect various illegal activities. Moreover, well-designed loss functions further enhance the prediction speed and the model\u2019s interpretability. Extensive experiments on three real-world datasets demonstrate that our proposed algorithm outperforms the state-of-the-art methods. Furthermore, additional case studies justify that our model not only explains existing illicit patterns but also can find new suspicious characters.<\/jats:p>","DOI":"10.1145\/3626102","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T16:07:54Z","timestamp":1695917274000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["From Asset Flow to Status, Action, and Intention Discovery: Early Malice Detection in Cryptocurrency"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-9728","authenticated-orcid":false,"given":"Ling","family":"Cheng","sequence":"first","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6077-4356","authenticated-orcid":false,"given":"Feida","family":"Zhu","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0092-0793","authenticated-orcid":false,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6266-2657","authenticated-orcid":false,"given":"Ruicheng","family":"Liang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2363-9018","authenticated-orcid":false,"given":"Huiwen","family":"Liu","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00105"},{"key":"e_1_3_2_3_2","article-title":"BitcoinHeist: Topological data analysis for ransomware detection on the Bitcoin blockchain","author":"Akcora Cuneyt Gurcan","year":"2019","unstructured":"Cuneyt Gurcan Akcora, Yitao Li, Yulia R. Gel, and Murat Kantarcioglu. 2019. BitcoinHeist: Topological data analysis for ransomware detection on the Bitcoin blockchain. arXiv preprint arXiv:1906.07852 (2019).","journal-title":"arXiv preprint arXiv:1906.07852"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101684"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39884-1_4"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-022-00864-6"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVCBT.2018.00014"},{"key":"e_1_3_2_8_2","first-page":"441","article-title":"Bitcoin and money laundering: Mining for an effective solution","volume":"89","author":"Bryans Danton","year":"2014","unstructured":"Danton Bryans. 2014. Bitcoin and money laundering: Mining for an effective solution. Indiana Law Journal 89 (2014), 441.","journal-title":"Indiana Law Journal"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3391195"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3398071"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3381036"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186046"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2905769"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101568"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2018.08.008"},{"key":"e_1_3_2_17_2","article-title":"Bitcoin Laundering: An Analysis of Illicit Flows into Digital Currency Services","author":"Fanusie Yaya","year":"2018","unstructured":"Yaya Fanusie and Tom Robinson. 2018. Bitcoin Laundering: An Analysis of Illicit Flows into Digital Currency Services. Center on Sanctions and Illicit Finance Memorandum, January (2018).","journal-title":"Center on Sanctions and Illicit Finance Memorandum, January"},{"key":"e_1_3_2_18_2","article-title":"Bitcoin transaction graph analysis","author":"Fleder Michael","year":"2015","unstructured":"Michael Fleder, Michael S. Kester, and Sudeep Pillai. 2015. Bitcoin transaction graph analysis. arXiv preprint arXiv:1502.01657 (2015).","journal-title":"arXiv preprint arXiv:1502.01657"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhz015"},{"key":"e_1_3_2_20_2","first-page":"749","volume-title":"Proceedings of the International Workshop on Complex Networks and Their Applications","author":"Maesa Damiano Di Francesco","year":"2016","unstructured":"Damiano Di Francesco Maesa, Andrea Marino, and Laura Ricci. 2016. An analysis of the Bitcoin users graph: Inferring unusual behaviours. In Proceedings of the International Workshop on Complex Networks and Their Applications. 749\u2013760."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.24251\/HICSS.2018.443"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/IISA.2019.8900698"},{"key":"e_1_3_2_23_2","article-title":"Auto-encoding variational Bayes","author":"Kingma Diederik P.","year":"2013","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).","journal-title":"arXiv preprint arXiv:1312.6114"},{"key":"e_1_3_2_24_2","unstructured":"Robin Klusman and Tim Dijkhuizen. 2018. Deanonymisation in Ethereum Using Existing Methods for Bitcoin. Retrieved October 5 2023 from https:\/\/rp.os3.nl\/2017-2018\/p61\/presentation.pdf"},{"key":"e_1_3_2_25_2","first-page":"491","volume-title":"Proceedings of the International Conference on Blockchain and Trustworthy Systems","author":"Li Ji","year":"2019","unstructured":"Ji Li, Chunxiang Gu, Fushan Wei, and Xi Chen. 2019. A survey on blockchain anomaly detection using data mining techniques. In Proceedings of the International Conference on Blockchain and Trustworthy Systems. 491\u2013504."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512226"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-9213-3_8"},{"key":"e_1_3_2_28_2","volume-title":"Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution (PACIS \u201919)","author":"Liang Jiaqi","year":"2019","unstructured":"Jiaqi Liang, Linjing Li, Shu Luan, Lu Gan, and Daniel Zeng. 2019. Bitcoin exchange addresses identification and its application in online drug trading regulation. In Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution (PACIS \u201919)."},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2020.2968376"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2020.00204"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.5555\/951949.952139"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467142"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403354"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449989"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1089\/big.2015.0056"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.180298"},{"key":"e_1_3_2_37_2","first-page":"25","volume-title":"Biometric and Surveillance Technology for Human and Activity Identification XII","author":"Monaco John V.","year":"2015","unstructured":"John V. Monaco. 2015. Identifying Bitcoin users by transaction behavior. In Biometric and Surveillance Technology for Human and Activity Identification XII. Vol. 9457. SPIE, 25\u201339."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/eCRS.2013.6805780"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44774-1_2"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418981.3418984"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1093\/cybsec\/tyz003"},{"key":"e_1_3_2_42_2","article-title":"Anomaly detection in the Bitcoin system\u2014A network perspective","author":"Pham Thai","year":"2016","unstructured":"Thai Pham and Steven Lee. 2016. Anomaly detection in the Bitcoin system\u2014A network perspective. arXiv preprint arXiv:1611.03942 (2016).","journal-title":"arXiv preprint arXiv:1611.03942"},{"key":"e_1_3_2_43_2","first-page":"534","volume-title":"Iberoamerican Congress on Pattern Recognition","author":"Prado-Romero Mario Alfonso","year":"2017","unstructured":"Mario Alfonso Prado-Romero, Christian Doerr, and Andr\u00e9s Gago-Alonso. 2017. Discovering Bitcoin mixing using anomaly detection. In Iberoamerican Congress on Pattern Recognition. Springer, 534\u2013541."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70278-0_16"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-4139-7_10"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05063-4_15"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.077"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2961675"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124373"},{"key":"e_1_3_2_50_2","article-title":"Identifying illicit accounts in large scale e-payment networks\u2014A graph representation learning approach","author":"Tam Da Sun Handason","year":"2019","unstructured":"Da Sun Handason Tam, Wing Cheong Lau, Bin Hu, Qiu Fang Ying, Dah Ming Chiu, and Hong Liu. 2019. Identifying illicit accounts in large scale e-payment networks\u2014A graph representation learning approach. arXiv preprint arXiv:1906.05546 (2019).","journal-title":"arXiv preprint arXiv:1906.05546"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-47854-7_4"},{"key":"e_1_3_2_52_2","article-title":"Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics","author":"Weber Mark","year":"2019","unstructured":"Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I. Weidele, Claudio Bellei, Tom Robinson, and Charles E. Leiserson. 2019. Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019).","journal-title":"arXiv preprint arXiv:1908.02591"},{"key":"e_1_3_2_53_2","article-title":"Detecting mixing services via mining Bitcoin transaction network with hybrid motifs","author":"Wu Jiajing","year":"2021","unstructured":"Jiajing Wu, Jieli Liu, Weili Chen, Huawei Huang, Zibin Zheng, and Yan Zhang. 2021. Detecting mixing services via mining Bitcoin transaction network with hybrid motifs. IEEE Transactions on Systems, Man, and Cybernetics: Systems 52, 4 (2021), 2237\u20132249.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_3_2_54_2","article-title":"Who are the phishers? Phishing scam detection on Ethereum via network embedding","author":"Wu Jiajing","year":"2022","unstructured":"Jiajing Wu, Qi Yuan, Dan Lin, Wei You, Weili Chen, Chuan Chen, and Zibin Zheng. 2022. Who are the phishers? Phishing scam detection on Ethereum via network embedding. IEEE Transactions on Systems, Man, and Cybernetics: Systems 52, 2 (2022), 1156\u20131166.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2877161"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258365"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_50"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011278"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9235003"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626102","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3626102","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:53:59Z","timestamp":1750287239000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3626102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,9]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4,30]]}},"alternative-id":["10.1145\/3626102"],"URL":"https:\/\/doi.org\/10.1145\/3626102","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,9]]},"assertion":[{"value":"2022-12-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-25","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-12-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}