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The workflow merges illicit proceeds into legitimate financial channels, aided by blockchain’s capability to rapidly and automatically disperse assets across wallets and exchanges. A machine-learning framework is proposed to analyze blockchain network activity and identify illicit transactions despite limitations in advanced pattern recognition. The VTAC approach uses pre-training, normalization, model training, and de-anonymization for effective detection, achieving 97.5% accuracy with XG Boost and outperforming prior methods; precision, recall, and F1-score exceed 95%, supporting investigator advisory use.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-in-money-laundering-detection-over-blockchain-technology/290489/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-in-money-laundering-detection-over-blockchain-technology/290489.png","ImageObject",300,407,{"name":92,"@type":93},"Aria","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",17,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the VTAC approach and what problem does it target?","Question",{"text":113,"@type":114},"VTAC (Value-driven-Transactional tracking Analytics for Crypto compliance) targets detection of illegal cryptocurrency transactions within blockchain networks, addressing gaps in advanced pattern recognition algorithms.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is VTAC designed to improve detection performance?",{"text":118,"@type":114},"It combines machine learning with a pre-training process, normalization, model training, and a de-anonymization step to analyze and identify illicit transactions effectively.",{"name":120,"@type":111,"acceptedAnswer":121},"What detection accuracy and evaluation metrics does the paper report?",{"text":122,"@type":114},"The approach reaches 97.5% accuracy using XG Boost, outperforming existing methods with up to 95.9% accuracy, and reports precision, recall, and F1-score consistently above 95%.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290489,1789641815,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},2336464648322,"https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488","Received 31 July 2024, accepted 25 August 2024, date of publication 29 August 2024, date of current version 14 January 2025. Digital Object Identifier 10.1109/ACCESS.2024.3452003  \nMachine Learning in Money Laundering Detection Over Blockchain Technology  \nALGIMANTAS VENČKAUSKAS1, ŠAR¯UNAS GRIGALI¯UNAS1,(Member, IEEE),  \nLINAS POCIUS 1, RASA BR¯UZGIEN˙E1, AND ANDREJS ROMANOVS2,(Senior Member, IEEE)  \n1Department of Computer Sciences, Kaunas University of Technology, 51368 Kaunas, Lithuania  \n2Information Technology Institute, Riga Technical University, 1048 Riga, Latvia  \nCorresponding author: Algimantas Venčkauskas ([algimantas.venckauskas@ktu.lt](algimantas.venckauskas@ktu.lt))  \nThis work was supported by the Project ‘‘Mission-Driven Implementation of Science and Innovation Programmes’’ funded by the Economic Revitalization and Resilience Enhancement Plan ‘‘New Generation Lithuania’’ under Grant 02-002-P-0001 .  \nABSTRACT Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.  \nINDEX TERMS Machine learning, blockchain, cybercrime, cryptocurrency, money laundering.  \nI. INTRODUCTION  \nCryptocurrencies are the most widely used in criminal activity for money laundering, fraud, theft, and carrying out dark web deals such as drug trafficking, weapon sales, and the sale of stolen personal information [1] . Cryptocurrencies facilitate a shadow economy on the dark web, allowing the purchase and sale of illicit goods and services without the oversight of regulatory bodies. Criminals use cryptocurrencies to hide the proceeds of their illegal activities [2] . Cybercriminals can use deceptive tactics like phishing, social engineering, or investment scams to trick individuals and persuade them to transfer their cryptocurrencies to malicious  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Tony Thomas.  \nentities. Cybercriminals might pose as legitimate businesses or create fake investment opportunities.  \nGiven that cryptocurrencies such as Bitcoin are simply digital codes, they often become the focus of hackers who utilise diverse hacking methods to undermine these assets. Attackers acquire unauthorised access to digital wallets by using techniques like phishing, which involves deceiving users into surrendering personal data, and by exploiting flaws in smart contracts or altering Blockchain protocols. Upon obtaining the private keys, anyone may effortlessly move the cryptocurrency to their own accounts. Moreover, t","cbCaiiYkVgfJOCRf","https://ap.wps.com/l/cbCaiiYkVgfJOCRf","pdf",3795618,"English","# Abstract\n# Introduction\n## Background: Cryptocurrencies in Cybercrime\n## Blockchain Security and Its Misuse\n## Need for Advanced Analytics and Detection","[{\"question\":\"What is the VTAC approach and what problem does it target?\",\"answer\":\"VTAC (Value-driven-Transactional tracking Analytics for Crypto compliance) targets detection of illegal cryptocurrency transactions within blockchain networks, addressing gaps in advanced pattern recognition algorithms.\"},{\"question\":\"How is VTAC designed to improve detection performance?\",\"answer\":\"It combines machine learning with a pre-training process, normalization, model training, and a de-anonymization step to analyze and identify illicit transactions effectively.\"},{\"question\":\"What detection accuracy and evaluation metrics does the paper report?\",\"answer\":\"The approach reaches 97.5% accuracy using XG Boost, outperforming existing methods with up to 95.9% accuracy, and reports precision, recall, and F1-score consistently above 95%.\"}]","Machine Learning in Money Laundering Detection Over Blockchain Technology | PDF",48]