{"paper_id":"3962f593-77b7-4a6d-8524-9ce722bd8250","body_text":"Blockchain Payment Fraud Detection with a Hybrid CNN-GNN-LSTM Model | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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 V3 Latest version Share on Blockchain Payment Fraud Detection with a Hybrid CNN-GNN-LSTM Model Authors : Haoran Zheng , Yuqing Lin , Qi He 0009-0000-6258-5137 , Yue Zou , and Han Wang Authors Info & Affiliations https://doi.org/10.22541/au.177153215.51813823/v3 275 views 226 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Blockchain payment fraud detection is hindered by extreme class imbalance, complex transaction relationships, and large-scale data. We propose a hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) networks to jointly model transaction features, graph topology, and temporal dynamics. The CNN extracts local feature interactions from transaction attributes, a Graph Attention Network (GAT) captures topological dependencies in the transaction graph, and a bidirectional LSTM models sequential behavior over time. A multihead attention module adaptively fuses these complementary representations for robust classification. To support scalable training on large-scale graphs, we employ a Random Sample Partition (RSP) strategy. Experiments on the Elliptic Bitcoin dataset show strong performance, achieving ROC-AUC 0.975, PR-AUC 0.918, and F1-score 0.902, outperforming competitive graphand sequence-based baselines. Ablation studies further confirm the contribution of each component. Overall, the proposed framework provides an accurate and scalable solution for detecting fraudulent activities in blockchain ecosystems. Supplementary Material File (blockchain payment fraud detection with a hybrid cnn-gnn-lstm model.pdf) Download 1.29 MB Information & Authors Information Version history V1 Version 1 19 February 2026 V2 Version 2 25 February 2026 V3 Version 3 27 February 2026 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords blockchain fraud detection graph neural network hybrid deep learning temporal model transaction graph Authors Affiliations Haoran Zheng View all articles by this author Yuqing Lin View all articles by this author Qi He 0009-0000-6258-5137 View all articles by this author Yue Zou View all articles by this author Han Wang View all articles by this author Metrics & Citations Metrics Article Usage 275 views 226 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Haoran Zheng, Yuqing Lin, Qi He, et al. Blockchain Payment Fraud Detection with a Hybrid CNN-GNN-LSTM Model. Authorea . 27 February 2026. DOI: https://doi.org/10.22541/au.177153215.51813823/v3 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.177153215.51813823/v3\",\"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:'9fe37a18c98ed04e',t:'MTc3OTE5NzY0Mw=='};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}