乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T05:16:48Z","timestamp":1725945408000},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2021,6]]},"abstract":"Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes, i.e., either based on the operation codes (opcodes) of the smart contract binaries or the transaction patterns of addresses. However, state-of-the-art approaches face several major limitations, including lacking interpretability and high false positive rates. Moreover, machine-learning based methods are susceptible to evasion techniques, and transaction-based techniques do not work on smart contracts that have a small number of transactions. These limitations render existing methods for detecting Ponzi schemes ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, by strictly following the definition of Ponzi schemes, we propose a heuristic-guided symbolic execution technique to first generate the semantic information for each feasible path in smart contracts and then identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.<\/jats:p>","DOI":"10.1145\/3460093","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T14:44:15Z","timestamp":1622817855000},"page":"1-30","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts"],"prefix":"10.1145","volume":"5","author":[{"given":"Weimin","family":"Chen","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Xinran","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Yuting","family":"Sui","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Ningyu","family":"He","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Haoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Lei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, HangZhou, China"}]},{"given":"Xiapu","family":"Luo","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/en.wikipedia.org\/wiki\/Gini_coefficient Accessed","author":"Gini","year":"2020","unstructured":"Gini coefficient. 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Dissecting ponzi schemes on ethereum: identification, analysis, and impact. arXiv: Cryptography and Security, 2017."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.08.014"},{"issue":"2","key":"e_1_2_1_19_1","article-title":"Dynamic efficiency, the riskless rate, and debt ponzi games under uncertainty","volume":"1","author":"Blanchard Olivier","year":"2001","unstructured":"Olivier Blanchard and Philippe Weil . Dynamic efficiency, the riskless rate, and debt ponzi games under uncertainty . The BE Journal of Macroeconomics , 1 ( 2 ), 2001 . Olivier Blanchard and Philippe Weil. Dynamic efficiency, the riskless rate, and debt ponzi games under uncertainty. The BE Journal of Macroeconomics, 1(2), 2001.","journal-title":"The BE Journal of Macroeconomics"},{"key":"e_1_2_1_20_1","volume-title":"Primal Wijesekera, and Mashael Al Sabah. 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