Blockchain Papers

Theodoros Daglis, Konstantinos Ν. Konstantakis, Georgios Lazarou, Panayotis G. Michaelides · 5 authors

No abstract is available for this record.

Neelkamal Chaudhary, Muhammad Shakeel Faridi

No abstract is available for this record.

Jairo Dote-Pardo, María Teresa Espinosa-Jaramillo

This policy paper reviews the evolution of research from 2016 to 2024 on the role of digital assets in illicit financial flows and evaluates the effectiveness of existing detection and prevention strategies. It highlights two central policy imperatives for Latin America: (i) fostering regional cooperation among central banks to harmonize anti-money-laundering standards and address regulatory arbitrage, and (ii) integrating supervisory technologies (SupTech and RegTech) into compliance frameworks to improve detection capacity. The paper concludes that combining technological innovation with coordinated regional regulation is essential to safeguard financial integrity in the digital-asset era.

Stefan Kitzler, Masarah Paquet-Clouston, Bernhard Haslhofer

The Decentralized Finance (DeFi) ecosystem has experienced over \$10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55% of crime events lead to significant negative price impacts, with an average decline of about 14%. Additionally, 68% of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74% of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.

Eva Harmelia Valentina, Kinza Aish

Objective: This research aims to develop a comprehensive framework to identify and prevent money laundering in Decentralized Finance (DeFi) by leveraging big data analytics, integrating advanced machine learning algorithms, and network analysis techniques to address the challenges of pseudonymity and decentralization inherent to this ecosystem. Research Design & Methods: This research utilizes a mixed method approach with machine learning analysis based on Elliptic Dataset and qualitative policy study, applying graph models and classification algorithms to detect illegal transactions with precision in the context of imbalanced data. Findings: The results show that the MLP and GCN models achieve high accuracy (98% and 97.3%) and excellent recall (99.5% and 99.4%) on the Elliptic Dataset, significantly outperforming traditional methods. Exploratory data analysis and graph visualization confirmed that illegal transactions form denser clusters and more complex paths, indicating a layering pattern. Implications and Recommendations: Theoretically, this research extends the application of big data and graph theory to new financial systems, providing a blueprint for future RegTech and FinTech research. Practically, the framework offers tangible tools for regulators, law enforcement, and DeFi platforms to enhance AML capabilities, supporting the development of real-time monitoring tools and risk assessment models. Contribution and Value Added: The main contribution of this research is the development of a robust and adaptive big data analytics-based AML framework, which effectively addresses the unique challenges of DeFi.

Noha E. El-Attar, Marwa Salama, Mohamed Abdelfattah, Sanaa Taha

Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories.

André Heitor de Faria Ferreira Lima

This dissertation investigates the crime of money laundering in the context of cryptoasset acquisition, from a functional-reductive perspective of criminal law. The central objective is to establish precise and coherent criteria for the incidence of criminal norms, aiming to limit the irrationality of punitive power and promote a counter-selective application, without compromising the accountability for complex criminal typologies. The study is pursued through a bibliographic review, analyzing national and international doctrine, legislation, and jurisprudence, complemented by interdisciplinary sources from economics, technology, and sociology. The first chapter establishes the theoretical premises of Zaffaroni's negative/agnostic theory of punishment and the reductive penal system. Criminal law, from this perspective, functions to limit punitive power. The second chapter delves into the crime of money laundering, analyzing its evolution, phases, the affected legal interest (socioeconomic order, with a focus on free competition and initiative), and its typical elements, based on the established theoretical framework. The third chapter comprehends the phenomenon of cryptoassets, detailing the functioning of Bitcoin and blockchain, the universe of decentralized finance (DeFi), forms of acquisition, and the Brazilian regulatory framework. The fourth chapter applies the dogmatic framework to the analysis of money laundering's typicality in different forms of cryptoasset acquisition and discusses the aggravating factor for the use of virtual assets. The research achieves its general objective by demonstrating a path for criminal dogmatics to establish precise criteria and limits for the incidence of money laundering, legitimizing judicial decisions by curbing punitive arbitrariness. Criteria are established to identify typical and atypical cases of money laundering involving cryptoasset acquisition, and requirements are set for the application of the special aggravating factor, conditioning it on the concrete demonstration that the use of cryptoassets intensified the harm to the legal interest. The interdisciplinary approach and consideration of the Brazilian reality are crucial to reject the trivialization of the institute and fulfill the counter-selective function of the penal system, preventing the automatic imputation of this serious crime without a proper technological understanding and knowledge of money laundering's limits

Hesam Sarkhosh, Uzma Maroof, Diogo Barradas

The rise of Web3 and Decentralized Finance (DeFi) has enabled borderless access to financial services empowered by smart contracts and blockchain technology. However, the ecosystem's trustless, permissionless, and borderless nature presents substantial regulatory challenges. The absence of centralized oversight and the technical complexity create fertile ground for financial crimes. Among these, money laundering is particularly concerning, as in the event of successful scams, code exploits, and market manipulations, it facilitates covert movement of illicit gains. Beyond this, there is a growing concern that cryptocurrencies can be leveraged to launder proceeds from drug trafficking, or to transfer funds linked to terrorism financing. This survey aims to outline a taxonomy of high-level strategies and underlying mechanisms exploited to facilitate money laundering in Web3. We examine how criminals leverage the pseudonymous nature of Web3, alongside weak regulatory frameworks, to obscure illicit financial activities. Our study seeks to bridge existing knowledge gaps on laundering schemes, identify open challenges in the detection and prevention of such activities, and propose future research directions to foster a more transparent Web3 financial ecosystem -- offering valuable insights for researchers, policymakers, and industry practitioners.

Md. Din, Muhammad Naufal, Abdul Rahman, Samsuria

Money laundering is regarded as an offense in nearly all nations and has evolved into a significant global concern. Despite the implementation of global anti-money laundering initiatives, its prevalence continues to rise. The need to control this phenomenon is paramount due to the substantial risks it poses to the financial system, economies, and society at large. Consequently, accountants play a pivotal role in the fight against money laundering by virtue of their proficiency in financial transactions, reporting, and auditing. This article discusses the involvement of accountants in combating money laundering, shedding light on the obstacles they encounter. These challenges encompass the lack of harmonization among multiple jurisdictions in their regulatory frameworks, the dilemma of breaching client confidentiality by divulging dubious business activities to law enforcement, the integration of digital payment systems with decentralized finance platforms, and the limited access to adequate resources and training programs. The advent of cryptography and digital technologies further complicates the detection of money laundering activities. Moreover, the article explores the measures undertaken by accountants to counter money laundering. These measures encompass a range of tasks aimed at prevention, including the establishment of compliance systems, detection, and reporting functions. The implementation of forensic accounting techniques and the enforcement of Anti-Money Laundering (AML) regulations form integral components of the prevention strategy. By adhering to regulatory standards, monitoring transactions, and collaborating with relevant authorities, accountants can safeguard businesses and the broader financial system from the detrimental impacts of money laundering. Their contribution extends beyond mere compliance, encompassing the fostering of a culture characterized by transparency, integrity, and ethical behavior within the realm of finance.

Shuyu Chang, Geng Chen, Haiping Huang, Rui Wang · 6 authors

No abstract is available for this record.

Arpita Nayak, Ipseeta Satpathy, Vishal Jain

Cryptocurrencies have revolutionized traditional finance by providing decentralized payment methods and disrupting global solutions. However, the increasing prevalence of cryptocrime threatens financial market security and public confidence. This study, “The Ripple Effect,” examines the financial damage caused by crypto-attacks on global payment networks and the regulatory complexities arising from deceitful cryptocurrency activity. It also examines the economic impact of cryptocrime, affecting individuals, organizations, and countries. The chapter highlights the financial threats that span across global markets and the regulatory barriers governments and organizations face. The fight against crypto crime requires international cooperation, sophisticated legal frameworks, and innovative blockchain analytical tools. The study emphasizes the need for transparency initiatives, education programs, and robust communication methods to restore trust within the crypto community.

Smriti Tandon Gupta, Eshan Singhal

The quickly advancing development of artificial intelligence (AI) and rapidly spreading cryptocurrencies established a modern environment for both legal developments and illegal practices. The analysis explores AI-related crime activities in cryptocurrencies through a study of AI technologies that assist and combat various crypto-based criminal operations. This paper investigates AI-driven crimes which include fraud alongside money laundering and cyberattacks and evaluates the dual capabilities of AI between criminal execution and prevention functions. This paper explains the ethical and legal risks of AI implementations in cryptocurrency crimes and provides recommendations for present and future research to tackle emerging threats.

Monika Kumari, Nikhil Kumar Goyal

This chapter explores the intersection of cryptocurrency crime and artificial intelligence (AI), highlighting both the threats posed by AI-driven cybercriminal activities and the potential of AI-based countermeasures. Cybercriminals increasingly leverage AI for money laundering, fraud, market manipulation, ransomware attacks, and identity theft, exploiting vulnerabilities in smart contracts and decentralized finance (DeFi) platforms. Conversely, AI is a powerful tool for combating these threats, aiding in blockchain analysis, anomaly detection, and Anti-Money Laundering (AML) enforcement. Advanced machine learning models enhance Know Your Customer (KYC) protocols and enable predictive crime prevention by analyzing transactional patterns. Additionally, this chapter examines challenges such as regulatory loopholes, adversarial AI, and jurisdictional complexities. The future implications of AI in financial crime prevention, including the role of quantum computing and emerging financial technologies, are also discussed.

Sina Ahmadi, Maral Mazjini

This chapter delves into the revolutionary emergence of Decentralized Finance (DeFi), its potential to revolutionize, and its vulnerabilities. Based on blockchain technology, DeFi bypasses conventional financial intermediaries through smart contracts to execute lending, trading, and other economic activities. The permissionless aspect of DeFi increases financial inclusion worldwide, especially for the unbanked and underbanked. Yet, this openness also exposes DeFi platforms to threats such as vulnerabilities in smart contracts, oracle manipulation, flash loan attacks, and governance attacks. Case studies like the Poly Network hack, Mango Markets manipulation, and Squid Game token rug pull illustrate these risks. The research covers technology innovations such as smart contract audits, formal verification, decentralized oracles, and AI-based threat detection to strengthen DeFi's future. It also examines how regulatory sandboxes and decentralized identification solutions can balance innovation and regulation.

Benjamin Appiah, Daniel Commey, Winful Bagyl-Bac, Laurene Adjei · 5 authors

Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior.

Cécile Volten, Michel van Eeten, Rolf van Wegberg

Abstract By converting between currencies, cryptocurrency exchanges provide access between the traditional and cryptocurrency ecosystem, making them susceptible to money laundering. The European Union extended the scope of the 5 $$^{\text {th}}$$ Anti-Money Laundering Directive (AMLD5) to include cryptocurrency exchanges, requiring them to obtain a registration, conduct customer due diligence, and report unusual transactions. It is, however, unknown whether the measures introduced by the implementation of AMLD5 lead to less risk exposure and what impact it has on cryptocurrency exchanges. This paper uses a mixed-methods approach to explore the effects of the Dutch implementation of AMLD5 measures on cryptocurrency exchanges active in the Netherlands. We analyzed over 335,000 transactions and complemented them with seven qualitative interviews with Dutch cryptocurrency exchanges and the supervisory authority. We find that the Dutch implementation of AMLD5 imposed high administrative burdens and substantial fees on relatively small exchanges that do not pose high money laundering risks. This raises questions about the alignment of the goals and consequences of the regulation.

Chee Hon Lew

This paper provides a comprehensive analysis of the evolving security risks and regulatory compliance challenges facing Bitcoin in financial applications. While Bitcoin's cryptographic foundations and decentralized architecture offer strong protections, emerging threats—such as consensus attacks, network exploits, privacy leaks, and operational vulnerabilities—pose significant risks to financial institutions. Simultaneously, global regulatory frameworks are tightening, particularly around AML, KYC, and consumer protection, compelling institutions to implement robust compliance strategies. The study synthesizes insights from computer science, finance, and law to propose integrated approaches combining technical safeguards with regulatory adherence. Strategies such as multi-signature controls, blockchain analytics, secure custody solutions, and “compliance by design” are explored. The paper concludes by discussing future risks including quantum computing and privacy-compliance tensions, emphasizing the need for adaptive risk management in institutional Bitcoin adoption.

Bahareh Parhizkari, Antonio Ken Iannillo, Christof Ferreira Torres, Sebastian Bănescu · 6 authors

No abstract is available for this record.

Yizhou Chen, Zeyu Sun, Guoqing Wang, Qingyuan Liang · 6 authors

Smart contracts have revolutionized the way transactions are executed, offering decentralized and immutable frameworks. The immutability of smart contracts poses significant risks when vulnerabilities exist in their code, leading to financial losses. Despite advancements in using deep learning for smart contract vulnerability detection (SCVD), existing methods struggle with the complex logic and intricate semantics embedded within smart contract code. Large Language Models (LLMs) have shown promise in providing deeper insights into smart contract logic. However, LLMs, such as GPT follow a decoder-only architecture and are trained in an unsupervised manner rather than learning specific labels. In the SCVD task, these LLMs have difficulty in capturing information related to vulnerabilities, leading to very low accuracy. Therefore, we propose CodeXplain, a novel SCVD approach that leverages the deep insights into code from LLM and the supervised learning capabilities of deep learning models to set the latest advance and performance. In particular, we deeply analyze 14 types of dangerous and common smart contract vulnerabilities. Based on the rationale of these vulnerabilities, nine perspective prompts are introduced to guide LLMs in generating code explanations that contribute to SCVD. Then, we propose a CodeT5-based semantic fusion module integrating smart contract code and code explanations. Finally, the performance of SCVD is improved by performing supervised learning on trusted labels. Experimental results on 3,544 real-world smart contracts demonstrate that CodeXplain outperforms 16 state-of-the-art SCVD methods, achieving an F1-score of 94.12% and an accuracy of 93.88%, surpassing all baselines.

Ioannis Sfyrakis, Paolo Modesti, Lewis Golightly, Minaro Ikegima

Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy.

Weijie Zhang, Ting Chen, Teng Li, Ze Wang · 6 authors

No abstract is available for this record.

Ryan Weege Achjian, Marcos A. Simplício

In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.

Rafael Santa Rosa Alves, Marco Amaral Henriques

A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de análise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisão de detecção (de 41,7% para 24,3%), levantando dúvidas sobre o real progresso das ferramentas.

Omar Rashid, Hatem hiad

The success of terrorist organizations in maintaining traditional resources to finance terrorist operations is sufficient to push them away from virtual currencies and their usual risks, as long as they are able to sell oil and transfer funds between their territories, and as long as their funds remain safe from attacks and persecution by the international community. Encrypted virtual currencies are characterized by high degrees of secrecy, privacy, and decentralization - and extremist religious groups And terrorism that adopts violence as a means of operation and expansion, and studying indicators indicating the growing importance of these currencies in circulation, exchange, and commercial transactions, nd in financing extremist religious groups and organizations, and financing the purchase of weapons and equipment used by these groups. It is a decentralized currency with no competent authority, and no central bank responsible for issuing it, and it is not subject to the restrictions of international banking and monetary institutions. This is a significant advantage that has attracted many individuals and groups to its circulation. Had international institutions and organizations been able to subject this currency to international oversight, or to a central authority, it would have lost its most important advantage, and terrorist and extremist organizations would have been unable to exploit it further.