Blockchain technology has been invented as a fundamental technique to the cryptocurrency Bitcoin in 2008, which is decentralized, consensus and cryptographic leger. However, due to the anonymity of the Blockchain, Bitcoin has been becoming one critical finance platform applied to transfer or hidden criminal income by offenders. Bitcoin crime refers to criminal activities which use Bitcoin as a criminal tools, criminal object or criminal settlement. Typical bitcoin crimes include online gambling, money laundering, fraud and more. To address these issues, our work aims to propose an efficient method to find transactions related with Bitcoin crime in the Bitcoin network. Which will efficiently support regulators to combat Bitcoin crimes. Through collecting and concluding kinds of Bitcoin crimes, we find several typical relation patterns among Bitcoin transactions with respect to crimes, and then construct and analysis a bitcoin criminal transaction network. At last, we study a graph neural network model with attention mechanisms to detect illegal transactions. Experimental results show that our method can achieve better classification accuracy, and has the ability to efficiently detect criminal clues and locate related illegal transactions.
Abstract New Technologies (blockchain, autonomous cars, artificial intelligence, and the internet of things) are revolutionizing industries such as finance, banking and transportation by improving efficiency and effectiveness in these businesses around the world. However, not much work is done on applying these technologies in the oil and gas industry. So, a tremendous opportunity exists to leverage these new technologies for possible applications in oil and gas. This paper explores the application of blockchain and smart contracts in managing an effective Management of Change (MOC) program and integrating it with operations to process real-time changes leading to faster and safer decision making. The paper is divided into two sections; the first provides an overview of the technology, and the second focuses on integrating smart contracts and blockchain technology to leverage the MOC program. This paper could be used to roll out new and improve existing MOC programs. The application of emerging technologies such as distributed ledger and smart contracts are still at the rudimentary level for oil and gas with few applications around trading and logistics. This paper unlocks the possible application in managing changes in the oil and gas, which will provide a solid platform for future innovations. The oil and gas industry are moving at a faster pace to achieve the next level of efficiency and effectiveness for sustainable growth, the key will remain in the early adoption of these emerging technologies for safer operation. The application mentioned in this paper will provide a company with an additional competitive advantage as they could effectively manage their data to make faster decisions; and achieve higher efficiency and safer operations simultaneously.
<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.
Distributed ledger technology benefits society by enabling an ecosystem of decentralised finance. However the pseudo-anonymised nature of transactions has also been an enabler of new routes for illicit activities ranging from individual scams to organised crimes. Current solutions for identifying addresses involved in illicit activities (illicit addresses) rely on commercial intelligence services, which are costly due to the intensive investigative efforts required. We propose Ledgit, an automatic real-time service for diagnosing illicit addresses on the Bitcoin blockchain. Ledgit is based solely on publicly available data, and uses an unsupervised clustering method that combines information from textual reports and the blockchain graph to assign a risk score that a Bitcoin address is involved in illicit activities. We verify the system with labeled addresses, showing high performance in identifying illicit addresses. Finally, we provide an intuitive user interface that provides accessible risk assessment with graph and report analytics.
What explains variation in the structure and practices of collective vigilantism? I develop a framework that focuses on relations among victims and between victims and the state. I use the framework to compare variation in collective vigilantism enacted by avocado and berry sectors in Michoac'n, Mexico. Centralized collective vigilantism by the avocado sector entailed a single sectoral organization coordinating victims' extra-legal activities with no interference from local politicians. By contrast, decentralized collective vigilantism by the berry sector consisted of multiple autonomous groups of victims in conflict with criminals, local political authorities and among each other as they competed for power and resources. These differences in collective vigilantism can be traced back to differences in the local political economies that shape relations among victims and between them and the state.
Hai Jin, Chenchen Li, Jiang Xiao, Teng Zhang · 6 authors
Due to the lack of supervision in the decentralized exchanges (DEXs), arbitrageurs can utilize information and take advantage of price gap to make profits over such platforms such as Ethereum blockchain. DEX arbitrage poses possibilities and opportunities for defrauding and can seriously impair the operation of the Ethereum ecosystem. It motivates this work to explore and characterize the unique features of arbitrage which differ from other frauds such as money laundering and Ponzi games for better detection. This work makes the first attempt for detecting arbitrage on Ethereum through feature fusion and positive-unlabeled learning (PU learning). We first conduct an in-depth analysis and exploit two-fold arbitrage features by fusion including: 1) statistical features that explicitly represent the node activity levels according to expert knowledge; and 2) structural features that implicitly encode the transactions information by graph machine learning. We then apply PU learning to generate negative instances for compensating the imbalanced arbitrage datasets. We evaluate our proposed method through extensive experiments over a real-world dataset and demonstrate that it can achieve 90% accuracy in detecting arbitrage activities on Ethereum.
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.
Purpose The purpose of this paper is to provide a high-level analysis of the intersection emerging cryptocurrency sector with anti-money laundering (AML) regulations and risk-based AML diligence systems maintained by financial institutions. Design/methodology/approach The analysis begins with a description of cryptocurrencies, focusing specifically on how the supporting technologies and applications increase vulnerabilities. The information will lay the foundation for examining the vulnerabilities existing in the architecture of cryptocurrency technology, as well as potential targets for regulations. The second part of the analysis will then shift focus to defining the scope of the money laundering problem associated with cryptocurrencies. An in-depth understanding of the problem is necessary to inform tailored AML legislation and regulations. The third part of the analysis will explore emerging AML regulations that govern cryptocurrencies, focusing specifically on those being developed and implemented in the United Arab Emirates (UAE). The UAE regulations will then be compared to those of the USA and European Union (EU) for comparative analysis and best practices. Findings The UAE has a robust legal system aimed at bolstering AML efforts while supporting widespread integration of crypto assets into business and government operations. A review of the UAEâs legislative framework reveals critical issues. First, the current regulations do not cover decentralized finance (DeFi) and non-fungible tokens (NFTs). The absence of clear regulations for DeFi and NFT protocols has created a leeway for money laundering and related criminal activities. Second, there is a high level of fragmentation in the UAEâs legislative landscape. The UAE does not have uniform, national laws that apply to all the Emirates. Fragmentation is not unique to the UAE but a major global problem that affects the USA and EU. Therefore, it is necessary to adopt a tailored approach where standard rules and regulations are responsive to the diverse aspects of cryptocurrencies. The strategy is vital, as it will be impractical to create a single legislation or law that will cover all the crypto assets, including their diverse applications. Furthermore, the Financial Action Task Force (FATF) should develop a global standard that will support a unified/harmonized application of AML/counter-terrorist financing (CTF) laws and regulations related to cryptocurrencies and the blockchain technology. Originality/value The borderless nature of digital currency and exchanges means that the existing laws and regulations are inadequate to address cross-border money laundering activities. Thus, there is an urgent need of harmonizing global regulations to ensure uniformity in applications. The quest for harmonization should be a priority as the FATF works towards developing a global standard. The global standard will support a uniform application of AML/CTF laws and regulations related to cryptocurrencies and the blockchain technology.
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.