Alexander Brechlin, Jochen Schäfer, Frederik Armknecht
ABSTRACT Cryptocurrency exchanges have become a multi‐billion dollar industry. Although these platforms are not only relevant for economic reasons but also from a privacy and legal perspective, empirical studies investigating the operations of cryptocurrency exchanges and the behavior of their users are surprisingly rare. A notable exception is a study analyzing the cryptocurrency exchange ShapeShift . While this study described new heuristics to retrieve a significant fraction of trades made on the plaform, its approach relied on identifying cryptocurrency transactions based on previously scraped trade data. This limited the analysis to the timeframe for which data had been acquired and likely led to false negatives in the transaction identification process. In this paper, we replicate and extend previous work by conducting an in‐depth investigation of the cryptocurrency exchange Evonax . Our analysis is based on actual trading data acquired by using a novel methodology allowing to extract detailed information from the public blockchain and the interface of the exchange platform. We are able to identify 30,402 transactions between the launch of Evonax in February 2018 and December 31, 2022, which should be close to a complete set of all transactions. This allows us not only to analyze the business practices of a cryptocurrency exchange but also to identify a number of interesting use cases that are likely to be associated with illegal activity. This paper is an extended version of a research article previously accepted at the CryptoEx Workshop at IEEE ICBC 2024.
Cryptocurrencies are a type of financial instrument that has been widely used by financial market participants since the early 2010s. Despite their growing popularity, their status within financial systems across different countries remains a topic of ongoing discussion. There is still no consensus on how to best understand the economic nature of these digital assets. This paper uses discourse analysis and content analysis to explore the various interpretations of cryptocurrencies’ economic nature. The paper argues that the interpretation of cryptocurrency’s economic nature depends heavily on the perspective of the stakeholder and the intended purpose of using the term. It considers arguments both for and against treating cryptocurrencies as commodities, currencies (including electronic and private currencies), or properties (assets, such as financial assets). It concludes that traditional cryptocurrencies do not meet the criteria for being considered money, and only central bank-issued digital currencies can fulfill all the functions associated with money. Decentralized cryptocurrencies, such as Bitcoin, cannot be classified as securities because there are no companies or organizations that issue these assets and bear any obligations under them. Instead, these assets have the characteristics of commodities. Different types of cryptocurrencies can be treated as either commodities or securities for tax purposes, depending on the specific circumstances. At the same time, assets with unique characteristics and behavior in the financial market may be included in a separate category for accounting purposes, or if the state allows for the use of cryptocurrencies in transactions without restrictions, they can be considered equivalent to cash.
Terrence August, Duy Dao, Kihoon Kim, Marius Florin Niculescu
Cryptocurrencies have prompted a shift away from classic security attacks toward ransomware-based extortion. To better understand the impact of cryptocurrencies on the cybersecurity landscape, we conduct a comparative analysis of cybersecurity metrics prior to and after the adoption of cryptocurrency using a series of connected software-use models in the presence of security externalities. In this framework, we endogenize the actions of both heterogeneous consumers and attackers, with entry of the latter being driven by both the size of the unpatched consumer population and, as a subset of it, the size of the ransom-paying consumer population. We first examine users’ adoption and patching behavior under both security scenarios. We explore how changes in attacker entry costs impact outcomes under both conventional and post-crypto ransomware threat landscapes. We show that ransomware scenarios may be more desirable than conventional ones when attacker entry costs are low, provided that the gains from entering with standard attacks under the ransomware scenario are not too high. However, under such scenarios, social welfare can increase under the same conditions that lead to larger ransoms being demanded and a higher expected total ransom being paid, which presents a conundrum to policymakers. We also examine the impact of market parameters associated with security losses from conventional attacks and residual losses when victims pay in ransomware attacks. This paper was accepted by Kay Giesecke, finance. Funding: This work was partially supported by Insung Research Grant of KUBS, the LG Yonam Foundation (of Korea), and an award from the Georgia Institute of Technology Center of International Business Education and Research as part of its funded research program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.00969 .
Stefano Ferretti, Gabriele D’Angelo, Vittorio Ghini
Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a crucial role in promoting societal welfare by mitigating the impact of criminal activities. This paper explores the application of Graph Neural Networks (GNNs) for classifying Bitcoin transactions. The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.
Marijana Joksimović, Marija Paunović, Stevica Dedjanski
<p>This paper examines the growing issue of money laundering through cryptocurrencies on a global scale. Criminals use digital assets to launder illicitly obtained funds, converting them into cryptocurrencies to obscure the origins of the money. Unlike traditional financial systems, decentralized finance (DeFi) platforms lack mechanisms to freeze or block funds from suspicious sources, presenting a unique challenge for law enforcement. However, the blockchain underlying cryptocurrencies allows for the tracking of transactions across DeFi protocols, making it possible to trace asset movement, albeit with difficulty due to complex methods criminals use to mix and transfer funds across multiple wallets. The study employs official data from financial institutions between 2019 and 2023, using time-series analysis to forecast money laundering trends under both optimistic and pessimistic scenarios. The paper concludes by highlighting the ongoing efforts by regulatory bodies to strengthen measures aimed at preventing cryptocurrency-related money laundering.</p> <p>In order to draw adequate conclusions, the data used in the paper are official data from financial institutions relevant to money laundering. The time series used in the paper includes data related to the period from 2016 to 2023 and the forecast model based on optimistic and pessimistic scenarios is constructed.</p>
The use of cryptocurrencies in transnational criminal activities has grown in recent years. The scholarly literature on cryptocurrencies recognizes this trend. Yet, there has been comparatively little attention paid to the degree to which cryptocurrencies pose a direct threat to U.S. homeland security interests. This article fills a gap in the scholarly literature on cryptocurrencies by presenting evidence that cryptocurrencies are a threat to U.S. homeland security interests, specifically because of their uses for financing terrorism, enabling human and drug trafficking, and evading international financial sanctions.
CrimRxiv, our flagship project, is dedicated to increasing the quantity, quality, and usage of "open criminology" resources.It's a hub and repository.For example, we publish and reshare open-access articles, books, data, code, and instructional resources.This initiative enhances the scientific rigor and impact of criminological research by ensuring free access for everyone. MOTIVATION & GOALSOpen access to criminological research can significantly improve public safety and justice systems globally.With this proposal, we aim to advance open criminology by onboarding our Members into DeFi, leveraging blockchain technology to enhance transparency and efficiency in research funding and dissemination.
Cryptocurrency investments are rapidly developing worldwide, including in Indonesia. Behind its profit potential, digital assets also open opportunities for criminals to commit money laundering offenses. The anonymity, pseudonymity, and decentralization of blockchain technology underlying cryptocurrencies create challenges for law enforcement in tracking illegal activities that exploit these assets. This study aims to examine the role of existing regulations in preventing the use of digital assets as a means of money laundering and to identify the challenges faced by law enforcement in enforcing rules against suspected cryptocurrency transactions. The research will analyze the extent to which the existing regulations, both at the national and international levels, are effective in preventing the use of cryptocurrencies for money laundering crimes. The second subtitle will explore various technical and legal constraints faced by law enforcement, including the lack of international cooperation, limitations of monitoring technology, and the low level of technical expertise among law enforcement officials.
This article offers a clear and approachable introduction to the evolving landscape of money and the frictions developing between traditional government control and decentralized finance (DeFi). Tailored for readers with a basic awareness of cryptocurrency but limited familiarity with its broader implications, the article demystifies DeFi by explaining its core concepts including blockchain, Centralized Bank Digital Currencies (CBDCs), and the historical role of government regulation of money through central banking. Against this backdrop, it examines the transformative potential of DeFi, emphasizing the growing tension between the centralized authority of governments and the decentralized ideals driving this new financial model. While governments seek to maintain stability and control, individuals increasingly gravitate toward the more affordable, efficient, and inclusive solutions promised by DeFi. Designed to empower readers with a better grasp of the forces shaping the future of finance, this article underscores the importance of understanding the delicate interplay between governmental oversight and decentralized innovation. As the digital economy expands, this dynamic struggle will influence not only economic policies but also person-al financial choices and access to resources.
This study examines why Bitcoin consistently traded at a discount in Colombia compared to the US market between April 2020 and July 2023. We introduced the “Bitcoin yield gap,” representing the difference between Bitcoin’s trading price in Colombia and its global price. Using robust ordinary least squares regression, we found a positive relationship between foreign exchange (FX) convenience yields and the yield gap, while Bitcoin convenience yields did not show a significant relationship. Additionally, Bitcoin network demand was significantly associated with the yield gap without affecting other regression parameters. These findings highlight that the yield gap is predominantly driven by dynamics in the domestic FX market, illustrating the interplay between traditional FX markets and the digital cryptocurrency landscape in Colombia.
Smart Contracts (SCs) handle transactions in the Ethereum blockchain worth millions of United States dollars, making them a lucrative target for attackers seeking to exploit vulnerabilities and steal funds. The Ethereum community has developed a rich set of tools to detect vulnerabilities in SCs, including reentrancy (RE) and unhandled exceptions (UX). A dataset of SCs labeled with vulnerabilities is needed to evaluate the tools’ efficacy. Existing SC datasets with labeled vulnerabilities have limitations, such as covering only a limited range of vulnerability scenarios and containing incorrect labels. As a result, there is a lack of a standardized dataset to compare the performances of these tools. Our dataset, SCRUBD, aims to fill this gap. SCRUBD is a dataset of real-world SCs and synthesized SCs labeled with RE and UX vulnerabilities. The real-world SC dataset is labeled through crowdsourcing, followed by manual inspection by an experienced SC programmer, and covers both RE and UX vulnerabilities. On the other hand, the synthesized dataset is carefully crafted to cover various RE scenarios only. Using SCRUBD, we compared the performance of six popular vulnerability detection tools. Based on our study, we found that Slither outperforms other tools on a crowdsourced dataset in detecting RE vulnerabilities, while Sailfish outperforms other tools on a manually synthesized dataset for detecting RE. For UX vulnerabilities, Slither outperforms all other tools.
The rise of blockchain and cryptocurrency networks has fueled financial innovation, yet it also presents new opportunities for illicit activities such as fraud and money laundering. Traditional detection approaches struggle with the non-Euclidean, large-scale nature of cryptocurrency transaction networks. Graph Neural Networks offer a promising solution for capturing complex relational data. This paper proposes four fusion architectures—Triple Parallel Layer, Hierarchical Staging, Attention-Weighted Residual Fusion, and Multi-View Feature Aggregation—combining Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network to enhance classification of illicit transactions. Experiments on the Elliptic Bitcoin dataset show that the proposed models achieve classification accuracies up to 97.17%, significantly outperforming standalone Graph Convolutional Network, Graph Attention Network, and Graph Isomorphism Network models. These results underscore the superior performance and robustness of the fusion architectures, with improvements in accuracy ranging from 1.1% to 2.9% over individual models, marking a step forward in financial crime detection within decentralized networks.
In this paper, we examine a novel category of services in the blockchain ecosystem termed Instant Cryptocurrency Exchange (ICE) services. Originally conceived to facilitate cross-chain asset transfers, ICE services have, unfortunately, been abused for money laundering activities due to two key features: the absence of a strict Know Your Customer (KYC) policy and incomplete on-chain data of user requests. As centralized and non-transparent services, ICE services pose considerable challenges in the tracing of illicit fund flows laundered through them. Our comprehensive study of ICE services begins with an analysis of their features and workflow. We classify ICE services into two distinct types: Standalone and Delegated. We then perform a measurement analysis of ICE services, paying particular attention to their usage in illicit activities. Our findings indicate that a total of 12,473,290 illegal funds have been laundered through ICE services, and 432 malicious addresses were initially funded by ICE services. Based on the insights from measurement analysis, we propose a matching algorithm designed to evaluate the effectiveness of ICE services in terms of efficiency and prevention of traceability. Our evaluation reveals that 92% of the user requests analyzed were completed in less than three minutes, underscoring the efficiency of ICE services. In addition, we demonstrate that the algorithm is effective in tracing illicit funds in situations where ICE services are used in malicious activities. To engage the community, the entire dataset used in this study is open-source.