{"id":3116,"date":"2025-09-22T11:04:51","date_gmt":"2025-09-22T15:04:51","guid":{"rendered":"https:\/\/www.yorku.ca\/research\/bccc\/?page_id=3116"},"modified":"2025-09-25T14:23:57","modified_gmt":"2025-09-25T18:23:57","slug":"defi-fraud-transactions-bccc-defifraudtrans-2025","status":"publish","type":"page","link":"https:\/\/www.yorku.ca\/research\/bccc\/ucs-technical\/cybersecurity-datasets-cds\/defi-fraud-transactions-bccc-defifraudtrans-2025\/","title":{"rendered":"DeFi Fraud Transactions (BCCC-DeFiFraudTrans-2025)"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<p>BCCC-DeFiFraudTrans-2025&nbsp;is a large-scale, Ethereum-based benchmark designed explicitly for profiling fraudulent and legitimate DeFi transactions. It contains&nbsp;<strong>1,026,867 annotated transaction samples<\/strong>&nbsp;spanning from 2017 to 2024, drawn from&nbsp;<strong>9,374 unique wallet addresses<\/strong>. The dataset integrates both wallet-level and transaction-level attributes, with&nbsp;<strong>79 features extracted via the DeFiTransLyzer-V1.0 analyzer<\/strong>. These features are organized into categories, including gas usage, cumulative gas consumption, token transfers, nonce behavior, block identifiers, transaction status, and error rates, providing a fine-grained view of transaction dynamics. Unlike many prior datasets, BCCC-FraudDefi-2025 was curated to be&nbsp;<strong>balanced, feature-rich, and validated<\/strong>: fraudulent labels were assigned based on Etherscan tags and then cross-checked with anomaly detection, consistency verification, and duplicate removal. The dataset enables evaluation of advanced fraud detection methods, including zero-day attacks, by eliminating reliance on prior wallet history and focusing purely on transaction-level behavior.<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<p><strong>The full research paper outlining the details of the dataset and its underlying principles:<br><\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:85em\">\n<p>\"<strong><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10207-025-01064-6?utm_source=rct_congratemailt&amp;utm_medium=email&amp;utm_campaign=nonoa_20250526&amp;utm_content=10.1007%2Fs10207-025-01064-6\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2096720925001034\">Advanced Genetic Algorithm and Penalty Fitness Function for Enhancing DeFi Security and Detecting Ethereum Fraud Transactions<\/a><\/strong>\"<\/p>\n\n\n\n<p>Arash Habibi Lashkari, Sepideh Hajihosseinkhani, Joshua Duarte, Isabella Lopez, Ziba Habibi Lashkari, Sergio Rios-Aguilar, Blockchain: Research and Applications, 2025<\/p>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<p><strong>Download Dataset:<\/strong><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:85em\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button bg-primary-2 text-white rounded-0 learnmore-block p-2\"><a class=\"wp-block-button__link has-text-color has-background no-border-radius wp-element-button\" href=\"https:\/\/www.yorku.ca\/research\/bccc\/ucs-technical\/cybersecurity-datasets-cds\/dataset-request\/\" style=\"color:#ffffff;background-color:#810001\" target=\"_blank\" rel=\"noreferrer noopener\">Request Dataset <i class=\"fas fa-chevron-right small pl-2\"><\/i><\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>BCCC-DeFiFraudTrans-2025&nbsp;is a large-scale, Ethereum-based benchmark designed explicitly for profiling fraudulent and legitimate DeFi transactions. It contains&nbsp;1,026,867 annotated transaction samples&nbsp;spanning from 2017 to 2024, drawn from&nbsp;9,374 unique wallet addresses. The dataset integrates both wallet-level and transaction-level attributes, with&nbsp;79 features extracted via the DeFiTransLyzer-V1.0 analyzer. These features are organized into categories, including gas usage, cumulative gas consumption, [&hellip;]<\/p>\n","protected":false},"author":253,"featured_media":0,"parent":349,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","footnotes":""},"tags":[8,38,51,12,13,16],"class_list":["post-3116","page","type-page","status-publish","hentry","tag-behaviour-centric-cybersecurity-center-bccc","tag-dataset","tag-defi-fraud-transactions-bccc-defifraudtrans-2025","tag-everyone","tag-public","tag-research"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>DeFi Fraud Transactions (BCCC-DeFiFraudTrans-2025) - Behaviour-Centric Cybersecurity Center (BCCC)<\/title>\n<meta name=\"description\" content=\"BCCC-DeFiFraudTrans-2025\u00a0is a large-scale, Ethereum-based benchmark designed explicitly for profiling fraudulent and legitimate DeFi transactions. 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