With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling have transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights.
<title>Abstract</title> In recent years, cryptocurrencies have been used as a new way to conduct transactions and transfer money among individuals, so the volume of daily transactions in their networks has reached to several billion dollars. The anonymity of users alongside the high security and privacy properties has led many criminals to the cryptocurrency networks to carry out their illegal transactions. However, public access to the blockchain of Bitcoin and many other cryptocurrencies, allows individuals and financial institutions to obtain information about some of these activities. Several approaches, such as investigating financial flow in the blockchain, statistical analysis, and machine learning methods, have been introduced to detect illegal transactions. This paper uses a deep learning model based on a graph convolutional network and multi-layer perceptron to classify Bitcoin transactions based on their applications. We extract several features from the transaction graph, then by doing some preprocessing on our data, we train a model, which is able to predict illicit transactions with an f1-score of 97.09% which outperforms previous approaches to this problem.
As the rules for countering money laundering constantly change, criminals find new methods and platforms to launder their âdirtyâ money. Recently, such new platforms have included the art market and the use of crypto currencies. Subsequently, both of these sectors were added to the list of sectors susceptible to facilitate money laundering. Apart from the traditional art market, criminals may use digital art in order to facilitate their activities. The rise of the digital art market with the expansion of Non-Fungible Tokens (NFTs) is a new area of concern for law enforcement agencies. Anonymity and price volatility of NFTs create a unique and exploitable environment for criminals. The complex nature and uncertain legal status of NFTs further complicate the counter measures one can take. This paper explains what NFTs are, analyses their relation to money laundering risks and scrutinises their legal status in the EU. In doing so, it identifies gaps in the law and training needs of law enforcement agencies. Finally, the paper provides potential solutions and recommendations in relation to these gaps. The paper offers a novel study on NFTs and aims to pave the way for further comparative studies related to NFTs.
We describe and analyze perishing mining, a novel block-withholding mining strategy that lures profit-driven miners away from doing useful work on the public chain by releasing block headers from a privately maintained chain. We then introduce the dual private chain (DPC) attack, where an adversary that aims at double spending increases its success rate by intermittently dedicating part of its hash power to perishing mining. We detail the DPC attack's Markov decision process, evaluate its double spending success rate using Monte Carlo simulations. We show that the DPC attack lowers Bitcoin's security bound in the presence of profit-driven miners that do not wait to validate the transactions of a block before mining on it.
Sean Foley, Bart Frijns, Alexandre Garel, TaiâYong Roh
We examine the relationship between national culture and a country's Bitcoin activity. Given that Bitcoin is a high-risk currency/investment that is frequently used for illegal purposes and whose market is relatively opaque, we focus on the cultural dimension of individualism, which has been related to financial market participation, risk-taking behavior, and overconfidence. Using unique data that includes the originating country for Bitcoin transactions, we examine the relationship between individualism and a country's Bitcoin activity for a sample of 80 countries between 2009 and 2020. We find a significant and positive relationship between a country's individualism and its use of Bitcoin consistent with cultural values affecting the demand for such high-risk currency/investments.
Mobeen Ur Rehman, Paraskevi Katsiampa, Rami Zeitun, Xuan Vinh Vo
This paper investigates the extreme dependence and risk spillovers between Bitcoin and the currencies of the BRICS and G7 economies. We find time-varying dependence between Bitcoin and all currencies. Moreover, when analysing risk spillovers from Bitcoin to currencies, we find that Bitcoin exercises significant power over most currencies, with the South African rand and Brazilian real holding both the highest downside and upside risk before and during the COVID-19 pandemic period, respectively. When considering risk spillovers from currencies towards Bitcoin, the Japanese yen exhibits the highest downside spillovers. Importantly, we find asymmetric spillovers between extreme upward and downward movements.
Tin Tironsakkul, Manuel Maarek, Andrea Eross, Mike Just
Bitcoin and other cryptocurrencies are well-known for their privacy properties that allow for the âanonymousâ exchange of money. Bitcoin tracking with taint analysis remains challenging as it does not account for the change in Bitcoins' ownership or the usage of Privacy-Enhancing Technologies (PETs) to obscure Bitcoins' movement, and often produces unessential incidents with transactions unlikely to be related to the targeted activity. In this paper, we propose to improve the Bitcoin taint analysis tracking process that adapts to the context of address ownership and avoid following unrelated transactions. First, we introduce an approach in which we incorporate Bitcoin taint analysis with address profiling. Second, we propose two context-based taint analysis strategies. Third, we introduce a set of metrics using hypothesised behaviours related to illegal Bitcoins and recognisable patterns within the blockchain. We conducted an experiment using sample data from known Bitcoin theft cases to illustrate and evaluate the approach. The results on address profile integration reveal distinct transaction behaviours in tracking theft cases following all the metrics, such as address reuse, address size and transaction fee payment. One of the context-based tracking strategies, Dirty-First, shows positive potential for illustrating illegal Bitcoinsâ spending and obscuring strategies. The majority of the six metrics we defined give distinct results in transaction behaviours between the theft cases and the control groups. Our context-based tracking methodology provides a solution for one of the shortcomings in the current Bitcoin tracking methodology and the next step for future cryptocurrency and cybercrime forensic research.
Bitcoin remains the most popular cryptocurrency and has attracted significant research attention, especially in the hedging and safe-haven literature. As many investors in bitcoin are concentrated heavily in cryptocurrencies as opposed to other assets, a question arises whether alternative cryptocurrencies (altcoins) can used as safe-havens and hedges against Bitcoin? We find that only meme coins offer hedging benefits but a wider range â Defi, meme coins, smart contracts, metaverse and privacy cryptocurrencies â can all act as safe-havens against bitcoin. We further show that their ability to act as hedges and safe-havens varies depending on whether the market is in a bubble or non-bubble period.
Christian Leuprecht, Caitlyn Jenkins, Rhianna Hamilton
Purpose This study aims to explain how cryptocurrency is leveraged for illicit purposes across the global financial system. Specifically, it establishes how cryptocurrency has been changing the nature of transnational and domestic money laundering (ML). It then assesses the effectiveness of conventional anti-money laundering (AML) policy and legislation against the proliferation of crypto laundering, using Canada as a critical case study. Design/methodology/approach Data was collected from court cases and secondary sources to build cross-case trends of cryptocurrency use in ML. Illicit International Political Economy forms the theoretical foundation for this study, whose contribution is situated in the current literature on crypto-ML. Findings This study finds that Bitcoin is common among crypto-money launderers, though most also use some form of alt-coin, and that the use of third-party currency exchanges is a prevalent method to create illicit funds and conceal proceeds of crime. The findings validate two hypotheses that illicit use of crypto is prevalent in the first two stages of ML, and that crypto is most often used in conjunction with other fiat currencies. Although law enforcement is improving on monitoring and understanding popular cryptocurrencies such as Bitcoin, alt-coins pose a significant challenge for criminal intelligence. New regulations for third-party currency exchanges are having a positive impact on curtailing crypto-laundering but are shown to be insufficient per se to contain the use of crypto in criminal activity. Originality/value This study contributes to a more robust understanding of the use of virtual currency in transnational and domestic ML. It contributes to an emerging body of literature on the role of technological change in enabling the global flow of illicit funds. It also informs public policy on virtual currency in general, and on AML regulation in Canada in particular.
Abstract In this article I explore the fundamental tension in the world of Bitcoin between âmaximalistsâ, who see Bitcoin as a tool for the promotion of a moral revolution, and âtradersâ, who approach Bitcoin pragmatically as a financial tool. Based on ethnography of a crypto gold rush that took place in the Bitcoin Embassy in Tel Aviv, I argue that, despite heuristic distinctions, both of these attitudes advance egalitarian tendencies. While maximalists offer a sense of belonging to a close-knit community of equals, traders promote the nominal equality of all value-making strategies in an open financial environment. I use the terms âideationalâ and âmaterialistâ to characterize these two modes of practice, which realize contemporary visions of egalitarian life in different forms.
Cryptocurrencies have grown to be very significant during the past two decades. Starting off at the way cryptocurrencies, specifically Bitcoin, operate, we move forward towards the discussion on their nature as currency and their disruptiveness. We navigate through the political character of Bitcoin over the years, the potential of Bitcoin and other cryptocurrencies to threaten a stateâs sovereignty and an overview of state responses to cryptocurrency. Finally, there is a number of speculations and suggestions on how Bitcoin could grow and gain a less controversial position in the global economy.
Within just four years, the blockchain-based Decentralized Finance (DeFi) ecosystem has accumulated a peak total value locked (TVL) of more than 253 billion USD. This surge in DeFi's popularity has, unfortunately, been accompanied by many impactful incidents. According to our data, users, liquidity providers, speculators, and protocol operators suffered a total loss of at least 3.24 billion USD from Apr 30, 2018 to Apr 30, 2022. Given the blockchain's transparency and increasing incident frequency, two questions arise: How can we systematically measure, evaluate, and compare DeFi incidents? How can we learn from past attacks to strengthen DeFi security? In this paper, we introduce a common reference frame to systematically evaluate and compare DeFi incidents, including both attacks and accidents. We investigate 77 academic papers, 30 audit reports, and 181 real-world incidents. Our data reveals several gaps between academia and the practitioners' community. For example, few academic papers address "price oracle attacks" and "permissonless interactions", while our data suggests that they are the two most frequent incident types (15% and 10.5% correspondingly). We also investigate potential defenses, and find that: (i) 103 (56%) of the attacks are not executed atomically, granting a rescue time frame for defenders; (ii) SoTA bytecode similarity analysis can at least detect 31 vulnerable/23 adversarial contracts; and (iii) 33 (15.3%) of the adversaries leak potentially identifiable information by interacting with centralized exchanges.
In the development of information and communication technology, there are many developments that occur. One of them is the emergence of NFTs as the latest trend in digital trading. This, of course, is a new breakthrough in the digital world. However, this can also be a loophole in committing money laundering crimes. In this research, we are using a qualitative descriptive approach with analytical methods. The result of this analysis is thatThe main problem with money laundering via NFTs and cryptocurrencies is the lack of understanding of the role of cryptocurrencies in financial crimes. As long as this misunderstanding is not addressed, the potential for the use of NFTs as a new method of financial crime will increase sharply to the point of endangering national security.
Bitcoin, regarded as a decentralized currency of the future as well as a digital gold, faces various challenges, such as scalability, the geographical concentration of mining, its politically informed design and history, its high market volatility, and inequalities in the proportion of accumulation. However, the number of Bitcoin owners has risen exponentially, and relevant socioeconomic and political groups have become increasingly diverse. Consequently, this article argues that what has contributed to the global diffusion of Bitcoin and its embeddedness in different human societies is its practical indeterminacy. Practical indeterminacy characterizes the fundamentally undefinable, indeterminate nature of Bitcoin's value, as it can change its form depending on who it encounters. In terms of temporality, practically indeterminate Bitcoin can urge potential owners and users to compare their pasts and futures, thus driving them to perceive, own, and use Bitcoin for their own purposes. By paying attention to the agency of Bitcoin, practical indeterminacy explains how individuals form their own relations with Bitcoin and how these relations lead to Bitcoin's further sociocultural embeddedness. The proliferation of such a wide range of humanâBitcoin relations shows that Bitcoin is not only monetary but also cultural, as it offers different meanings to users and owners.
The core of many cryptocurrencies is the decentralised validation network operating on proof-of-work technology. In these systems, validation is done by so-called miners who can digitally sign blocks once they solve a computationally-hard problem. Conventional wisdom generally considers this protocol as secure and stable as miners are incentivised to follow the behaviour of the majority. However, whether some strategic mining behaviours occur in practice is still a major concern. In this paper we target this question by focusing on a security threat: a selfish mining attack in which malicious miners deviate from protocol by not immediately revealing their newly mined blocks. We propose a statistical test to analyse each miner's behaviour in five popular cryptocurrencies: Bitcoin, Litecoin, Monacoin, Ethereum and Bitcoin Cash. Our method is based on the realisation that selfish mining behaviour will cause identifiable anomalies in the statistics of miner's successive blocks discovery. Secondly, we apply heuristics-based address clustering to improve the detectability of this kind of behaviour. We find a marked presence of abnormal miners in Monacoin and Bitcoin Cash, and, to a lesser extent, in Ethereum. Finally, we extend our method to detect coordinated selfish mining attacks, finding mining cartels in Monacoin where miners might secretly share information about newly mined blocks in advance. Our analysis contributes to the research on security in cryptocurrency systems by providing the first empirical evidence that the aforementioned strategic mining behaviours do take place in practice.
In our rapidly globalizing and digitalizing world, data transfer can be done quickly in multimedia, communication, computer systems, etc. On the other hand, a blockchain database is a technology that allows us to transfer assets such as digital money that we attribute value to. This technology, which eliminates centralization and distributes reliability and transparency among all users, is also known as the technology under virtual currencies such as Bitcoin and Ethereum. El-Salvador is the first country to accept Bitcoin as a legal currency. It is one of the most fundamental issues to wonder how this will affect the economy of El-Salvador and the spread of financial services to all members of society. In this study, it is examined how the process of accepting Bitcoin as legal money has an effect on the economy of El-Salvador and the percentage of people's access to financial services. Although there is not a great background to make an empirical assessment on the subject, it seems that a large part of the society has started to use Bitcoin through the official mobile application of the state. It is seen that even citizens who did not have a bank account before can access financial services with this method. In addition, the rapid increase in the investments coming to the country due to the tax advantages provides a great added value for the country's economy.
Cryptocurrency has become ubiquitous and is evolving constantly. The question is if our legal framework is catching up with it. Therefore, this article analyzes the arguments on the legitimacy and legality of cryptocurrency in order to emphasize the relation between corruption and cryptocurrency. The research has enlightened some cogent arguments on the possibility of perpetrators committing corruption acts through cryptocurrency. These arguments basically refer to some of the unique characteristics of cryptocurrency such as the quick value fluctuation, the difficulties in tractability and the lacking current legislation. The unique features of it may headline cryptocurrency as an immensely attractive environment for corruption activities. Hence the world has already faced some tangled scamming scandals with cryptocurrency specified herein. Therefore, the aim of this article is to highlight the possibility for corruption acts to be committed through cryptocurrency as a form of corruption unknown before.
Jinho Choi, Jaehan KIM, Minkyoo Song, Hanna KIM · 8 authors
Cryptocurrency abuse has become a critical problem. Due to the anonymous nature of cryptocurrency, criminals commonly adopt cryptocurrency for trading drugs and deceiving people without revealing their identities. Despite its significance and severity, only few works have studied how cryptocurrency has been abused in the real world, and they only provide some limited measurement results. Thus, to provide a more in-depth understanding on the cryptocurrency abuse cases, we present a large-scale analysis on various Bitcoin abuse types using 200,507 real-world reports collected by victims from 214 countries. We scrutinize observable abuse trends, which are closely related to real-world incidents, to understand the causality of the abuses. Furthermore, we investigate the semantics of various cryptocurrency abuse types to show that several abuse types overlap in meaning and to provide valuable insight into the public dataset. In addition, we delve into abuse channels to identify which widely-known platforms can be maliciously deployed by abusers following the COVID-19 pandemic outbreak. Consequently, we demonstrate the polarization property of Bitcoin addresses practically utilized on transactions, and confirm the possible usage of public report data for providing clues to track cyber threats. We expect that this research on Bitcoin abuse can empirically reach victims more effectively than cybercrime, which is subject to professional investigation.
Abstract Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.
Pedro Bustamante, Meina Cai, Marcela Gomez, Colin Harris · 13 authors
Studies of blockchain governance can be divided into analyses of the governance of blockchains (such as rules and power dynamics within a given network) and governance by blockchains (such as how blockchains can be implemented to improve self-governance of community-based peer production networks). Less emphasis has been placed on applications of distributed ledgers to public sector governance. Our review clarifies that the decentralization and distributive features that enable blockchains to link up loosely connected private organizations and public agencies to improve efficiency and transparency of government transactions. However, most blockchain applications lack clear advantages over the conventional digital recording of information. In addition, our review highlights that blockchain applications in public sector governance are potentially vast, though in most instances, the existing applications have not extended much beyond limited-scale pilots. We conclude with a call for the construction of indexes of public sector implementations of blockchains, as none yet exist, as well as for additional research to understand why governments have not deployed blockchains more widely.
Abstract Elliptic dataâone of the largest Bitcoin transaction graphsâhas admitted promising results in many studies using classical supervised learning and graph convolutional network models for anti-money laundering. Despite the promising results provided by these studies, only few have considered the temporal information of this dataset, wherein the results were not very satisfactory. Moreover, there is very sparse existing literature that applies active learning to this type of blockchain dataset. In this paper, we develop a classification model that combines long-short-term memory with GCNâreferred to as temporal-GCNâthat classifies the illicit transactions of Elliptic data using its transactionâs features only. Subsequently, we present an active learning framework applied to the large-scale Bitcoin transaction graph dataset, unlike previous studies on this dataset. Uncertainties for active learning are obtained using Monte-Carlo dropout (MC-dropout) and Monte-Carlo based adversarial attack (MC-AA) which are Bayesian approximations. Active learning frameworks with these methods are compared using various acquisition functions that appeared in the literature. To the best of our knowledge, MC-AA method is the first time to be examined in the context of active learning. Our main finding is that temporal-GCN model has attained significant success in comparison to the previous studies with the same experimental settings on the same dataset. Moreover, we evaluate the performance of the provided acquisition functions using MC-AA and MC-dropout and compare the result against the baseline random sampling model.
2017 is the year when the cryptocurrencies came into limelight with the primary trading in bitcoin being featured on a mainstream market in Chicago, Since then many people started investing into bitcoins or at least wanting to know about bitcoin, mostly including the people from the software industry. In this article it is clearly defined in a stepwise order using meaningful pictures and figures, from the very beginning of cryptocurrencies to the technology behind bitcoin, that is the blockchain technology. Most people do not trust in the security of trading in cryptocurrencies, but after understanding the working of Blockchain one would definitely believe in the technical security of using a cryptocurrency but the financial reasoning to investment in cryptocurrencies is a whole another story.
Abstract In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of publicâprivate synergies to devise forensic strategies conscious of the spirit of explainability and data openness.