{"paper_id":"2b32eebb-0de7-4385-861c-250ef2bbdbf9","body_text":"Detection of Blockchain Online Payment Fraud Via CNN-LSTM | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 February 2026 V6 Latest version Share on Detection of Blockchain Online Payment Fraud Via CNN-LSTM Authors : Keyu Yuan , Yuqing Lin , Wenjun Wu , and Chia Hong Chang 0009-0004-2689-1806 Authors Info & Affiliations https://doi.org/10.22541/au.176851576.63306241/v6 272 views 214 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Criminal activities such as money laundering, scams, and even terrorist financing are growing common targets for malicious hackers exploiting blockchain-based payment systems, e.g., involving cryptocurrencies such as Bitcoin. Distinguishing between legitimate and suspicious transactions in such activities is difficult without generating many false positives, which is even more complicated with imbalanced datasets and when graph-like structures (e.g., transaction flows) are involved. Existing techniques are not able to effectively and conveniently capture the (sequential and local) patterns of transaction flows.We introduce a hybrid CNN-LSTM model which contains three main building blocks: CNN for mining local features from transaction graphs, LSTM for risk evaluation through a sequence of anomaly forecasting and finally an economic minimization module to realize the minimization of economic losses. Experiments on the Elliptic Bitcoin Dataset with 2% labels for illicit activities among 203,769 transactions demonstrate that our model decreases the expected loss by 45% compared to the rule-based benchmark model while keeping the rate of false positive risks below 0.5% .As such, we are able to seamlessly integrate detection, economic modelling and risk together in the model and show that it is possible to create a tool for real world security analysis of blockchains. CCS CONCEPTS Mathematics of computing ~Probability and statistics ~Probabilistic inference problems Supplementary Material File (detection of blockchain online payment fraud via cnn-lstm - arxiv.pdf) Download 1.30 MB Information & Authors Information Version history V1 Version 1 15 January 2026 V2 Version 2 23 January 2026 V3 Version 3 09 February 2026 V4 Version 4 19 February 2026 V5 Version 5 25 February 2026 V6 Version 6 27 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords anomaly detection cnn-lstm fraud detection on blockchain risk managment in cryptocurrencies Authors Affiliations Keyu Yuan View all articles by this author Yuqing Lin View all articles by this author Wenjun Wu View all articles by this author Chia Hong Chang 0009-0004-2689-1806 View all articles by this author Metrics & Citations Metrics Article Usage 272 views 214 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Keyu Yuan, Yuqing Lin, Wenjun Wu, et al. Detection of Blockchain Online Payment Fraud Via CNN-LSTM. Authorea . 27 February 2026. DOI: https://doi.org/10.22541/au.176851576.63306241/v6 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(\".js__slcInclude\").on(\"change\", function(e){ if ($(this).val() == 'refworks') $('#direct').prop(\"checked\", false); $('#direct').prop(\"disabled\", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {\"doi\":\"10.22541/au.176851576.63306241/v6\",\"type\":\"Article\"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob(\"bG9jYXRpb24=\"),_bnb=atob(\"b3JpZ2lu\"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(\" \")); $.get(\"/resource/lodash?t=\"+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML=\"window.__CF$cv$params={r:'9fdf2f817b54c13d',t:'MTc3OTE1MjY0NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);\";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();","source_license":"CC-BY-4.0","license_restricted":false}