{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:44:03Z","timestamp":1783010643954,"version":"3.54.6"},"reference-count":114,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"name":"the National Key R&D Program of China","award":["2022YFE0113200"],"award-info":[{"award-number":["2022YFE0113200"]}]},{"name":"the Innovation and Technology Commission of Hong Kong (ITC) under Mainland-Hong Kong Joint Funding Scheme (MHKJFS) under Grant","award":["MHP\/135\/23"],"award-info":[{"award-number":["MHP\/135\/23"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>The pseudo-anonymity and rapidly expanding ecosystem of Decentralized Finance (DeFi) have brought about significant liquidity on EVM-compatible blockchains, making them lucrative targets for cybercriminals. In the modern financial landscape, the need for an automated, high-speed, and effective illicit money tracing system is more urgent than ever to support regulators, on-chain service providers and security practitioners in their efforts to combat the frequent and large-scale occurrences of cyber financial crimes.<\/jats:p>\n                  <jats:p>\n                    In this paper, we propose\n                    <jats:sc>MFTracer,<\/jats:sc>\n                    an automated system for tracing illicit money flows on EVM-compatible blockchains. Against the backdrop of a domain where tracing remains labor-intensive and expert-driven,\n                    <jats:sc>MFTracer<\/jats:sc>\n                    is developed in response to two pressing real-world demands: operational\n                    <jats:italic toggle=\"yes\">efficiency<\/jats:italic>\n                    and forensic\n                    <jats:italic toggle=\"yes\">effectiveness.<\/jats:italic>\n                    In response to the sophisticated fund transfer mechanisms enabled by the EVM environment, we introduce a novel fine-grained technique that enables protocol-agnostic transaction-level fund flow analysis. We further propose MFA, a lightweight and purpose-built graph abstraction with a tailored storage backend, to support efficient data retrieval. We also present a simulation algorithm for downstream illicit flow discovery. We implemented\n                    <jats:sc>MFTracer.<\/jats:sc>\n                    Its infrastructure for data retrieval achieves 3.7\u00d7 to 9.4\u00d7 higher storage efficiency while being 14.1\u00d7 to 300\u00d7 faster than the leading graph database systems. Furthermore, applied to real-world cybercrime incidents,\n                    <jats:sc>MFTracer<\/jats:sc>\n                    achieved 94.09% coverage of illicit money flows. It also newly reported 686 blockchain addresses and 4183 related transactions involved in money laundering that were previously undiscovered.\n                    <jats:sc>MFTracer<\/jats:sc>\n                    was able to reconstruct complete fund flow trajectories and provide strong evidence to investigators for 120.9 million in stolen assets.\n                  <\/jats:p>","DOI":"10.1145\/3771578","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T20:07:03Z","timestamp":1764706023000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Shedding Light on Shadows: Automatically Tracing Illicit Money Flows on EVM-Compatible Blockchains"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2226-8749","authenticated-orcid":false,"given":"Yicheng","family":"Huo","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5132-3284","authenticated-orcid":false,"given":"Yufeng","family":"Hu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7610-4736","authenticated-orcid":false,"given":"Yajin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7054-5773","authenticated-orcid":false,"given":"Ting","family":"Yu","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1675-5283","authenticated-orcid":false,"given":"Lei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0547-315X","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,2]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Atomic Wallect Hack. https:\/\/atomicwallet.io\/blog\/articles\/june-3rd-event-statement Referenced","year":"2025","unstructured":"2023. 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