Darcy W E Allen, Chris Berg, Sinclair Davidson, Jason Potts
Knowledge about property rights is a commons that facilitates market exchange and economic coordination. The governance of that shared knowledge resource—the various formal and informal rules that maintain ledgers of property rights—ranges from community norms to formal state registries. In this chapter we make three contributions. First, we use the lens of knowledge commons theory to argue that knowledge about property rights is a shared resource. Second, we explore how that knowledge commons is governed—particularly relating to “rules in use”—might shift due to technological advances in distributed ledgers. Third, we argue that as blockchain augments and complements existing governance structures, it creates a more robust political economy.
Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
In supply chains where stakeholders belong to the economically disadvantaged segment and form an important part of the supply chain distribution, the complexities grow manifold. Fisheries in developing nations are one such sector where the complexity is not only due to the produce being perishable but also due to the livelihood dependence of others in the coastal regions that belong to the section of economically disadvantaged. This paper explains the contextual challenges of fish supply chain in a developing country and describes how integrating disruptive technologies can address those challenges. Through a positive deviance approach, we show how firms can help unorganized supply chains with economically disadvantaged suppliers by carefully redesigning the supply chain through the integration of satellite imagery and blockchain technology. With COVID-19 in the backdrop, we highlight how such technologies significantly improves the supply chain resilience and at the same time contributes to the income generating opportunities of poor fisherfolks in developing nations. Our study has important implications to both developing markets and food supply chain practitioners as this paper tackles issues such as perishability, demand-supply mismatch, unfair prices, and quality related data transparency in the entire value chain.
Abstract Blockchain technology has a great potential for improving public administration – its transparency and efficiency. It is also discussed as an instrument for reducing corruption and transaction costs. This paper discusses the potential use of block-chain technology in public administration. It is based on a case-study approach focusing on real estate registration in Kazakhstan. Particular attention is paid to identifying factors hindering the development of the blockchain technology. The paper indicates that the main barriers to further use of blockchain technology in Kazakhstan are insufficient legislation and also the complexity of the technical implementation of blockchain projects and integration with existing systems.
Abstract The pronounced multi-domain technologicalization specific to the last decades has had a significant impact on all areas of activity, including the financial one. The use of cyberspace to facilitate the actions undertaken in the monetary activity has generated the development of this field to the point where virtual currencies have been created and new technologies have been developed to support their use. Like any emerging domain, the cryptocurrency field and the related technology are in a relatively early stage and exclusively imply operating in cyberspace, thus generating security risks in the event of the involvement of malicious entities in illicit activities. In this context, it is worth analyzing how the improper use of the crypto domain can lead to various risks to national security.
Deepesh Chaudhari, Rachit Agarwal, Sandeep K. Shukla
The temporal aspect of blockchain transactions enables us to study the address's behavior and detect if it is involved in any illicit activity. However, due to the concept of change addresses (used to thwart replay attacks), temporal aspects are not directly applicable in the Bitcoin blockchain. Several pre-processing steps should be performed before such temporal aspects are utilized. We are motivated to study the Bitcoin transaction network and use the temporal features such as burst, attractiveness, and inter-event time along with several graph-based properties such as the degree of node and clustering coefficient to validate the applicability of already existing approaches known for other cryptocurrency blockchains on the Bitcoin blockchain. We generate the temporal and non-temporal feature set and train the Machine Learning (ML) algorithm over different temporal granularities to validate the state-of-the-art methods. We study the behavior of the addresses over different time granularities of the dataset. We identify that after applying change-address clustering, in Bitcoin, existing temporal features can be extracted and ML approaches can be applied. A comparative analysis of results show that the behavior of addresses in Ethereum and Bitcoin is similar with respect to in-degree, out-degree and inter-event time. Further, we identify 3 suspects that showed malicious behavior across different temporal granularities. These suspects are not marked as malicious in Bitcoin.
In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is important to identify hedge or safe haven assets. Therefore, this paper analyzes changes in the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For this purpose, the M-GARCH model employed considers the interdependence among all the portfolio assets by using a time-varying asset pricing framework. This framework measures the impact of commodities and global stock prices as sources of systemic risk for Bitcoin returns before and after the COVID-19 pandemic. The evidence suggests that during the COVID-19 pandemic, the effects of changes in commodities and global prices on the idiosyncratic risk of Bitcoin were statistically significant. The idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted for by the proposed model rose from 86.06% to 95.05% during the pandemic. These results are in line with previous studies regarding the properties of Bitcoin as a hedge or safe haven asset for a portfolio composed of commodities and global stocks.
Purpose This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing funds to multiple accounts and then reexchanging the crypto back. This process of exchanging currencies is done through cryptocurrency exchanges. Current preventive efforts are outdated, and ML may provide novel ways to identify illicit currency movements. Hence, this study investigates ML applicability for combatting money laundering activities using cryptocurrency. Design/methodology/approach Four supervised-learning algorithms were compared using the Bitcoin Elliptic Dataset. The method covered a quantitative analysis of the algorithmic performance, capturing differences in three key evaluation metrics of F1-scores, precision and recall. Two complementary qualitative interviews were performed at cryptocurrency exchanges to identify fit and applicability of the algorithms. Findings The study results show that the current implemented ML tools for preventing money laundering at cryptocurrency exchanges are all too slow and need to be optimized for the task. The results also show that while not one single algorithm is most suitable for detecting transactions related to money-laundering, the specific applicability of the decision tree algorithm is most suitable for adoption by cryptocurrency exchanges. Originality/value Given the growth of cryptocurrency use, this study explores the newly developed field of algorithmic tools to combat illicit currency movement, in particular in the growing arena of cryptocurrencies. The study results provide new insights into the applicability of ML as a tool to combat money laundering using cryptocurrency exchanges.
Crime research has repeatedly shown that small proportions of offenders are responsible for large proportions of crimes. While there is a substantial body of evidence for this ‘offending concentration’ in connection to traditional offline crime, there is limited research assessing the concentration of offending for cybercrime. This research analyzes victim reports of Bitcoin-related cybercrimes (blackmail, ransomware, sextortion, darknet market fraud, Bitcoin tumbler fraud) to illuminate the extent of cybercrime offending concentration and to identify groups of offenders involved in online crime. Our results indicate that a large proportion of cybercrimes are associated with a small number of very active Bitcoin addresses. However, Bitcoin addresses associated to high numbers of reports are not necessarily those that generate the largest financial benefits.
Abstract The rise of cryptocurrencies during the last decade has caused growing concerns among national and international regulators. One of the risks identified is that these instruments may constitute an innovative tool for criminals when laundering money. This risk has been confirmed by numerous recent cases which have underlined the criminogenic potential of cryptocurrencies. Through the V antimoney laundering (AML) Directive, the European legislator has first regulated this emerging issue. This legislation extends the AML duties to two players of the cryptocurrencies market: exchangers and wallet providers. This choice, however, does not exploit the opportunities offered by cryptocurrencies and fails to provide a customized regulatory framework. By maintaining a traditional regulatory approach centered on intermediaries it misses the key innovation of blockchain technology: disintermediation. Compared with traditional online money flows, intermediaries are not necessary nor fundamental in the cryptocurrencies environment. Failing to adapt to this reality, the Directive is employing chivalry to fight a trench war. To guarantee the integrity of this market, the policymaker has to abandon the traditional intermediary‐centred approach in favor of a strategy that seizes the new opportunities offered by blockchain. This paper advocates for a shift from an individual‐centered approach to financial crime control to a transaction‐centered one.
Kevin Tjiam, Rui Wang, Huanhuan Chen, Kaitai Liang
Smart contracts on Ethereum enable billions of dollars to be transacted in a decentralized, transparent and trustless environment. However, adversaries lie await in the Dark Forest, waiting to exploit any and all smart contract vulnerabilities in order to extract profits from unsuspecting victims in this new financial system. As the blockchain space moves at a breakneck pace, exploits on smart contract vulnerabilities rapidly evolve, and existing research quickly becomes obsolete. It is imperative that smart contract developers stay up to date on the current most damaging vulnerabilities and countermeasures to ensure the security of users' funds, and to collectively ensure the future of Ethereum as a financial settlement layer. This research work focuses on two smart contract vulnerabilities: transaction-ordering dependency and oracle manipulation. Combined, these two vulnerabilities have been exploited to extract hundreds of millions of dollars from smart contracts in the past year (2020-2021). For each of them, this paper presents: (1) a literary survey from recent (as of 2021) formal and informal sources; (2) a reproducible experiment as code demonstrating the vulnerability and, where applicable, countermeasures to mitigate the vulnerability; and (3) analysis and discussion on proposed countermeasures. To conclude, strengths, weaknesses and trade-offs of these countermeasures are summarised, inspiring directions for future research.
Bitcoins are evolving as a modern class of investment assets and it is crucial for investors to manage their investment risk. This paper examines the impact of macroeconomic-financial indicators on Bitcoin price using symmetric and asymmetric version of autoregressive distributed lag (ARDL) models with structural breaks. The asymmetric long-run association ascertained between Bitcoin prices and the macroeconomic-financial indicators is evident. Our empirical results indicate that the Bitcoin cannot be used to hedge against the inflation, Federal funds rate, stock markets and commodity markets. We further find that Bitcoin can be regarded as a hedging device for the oil prices. Our findings have significant implications for market participants who consider including alternate investment assets in their portfolios.
On January 26, 2018, 58 billion yen ($530 million) worth of a cryptocurrency, NEM, was fraudulently accessed, and was then stolen from the Coincheck Exchange, headquartered in Japan. This hacking incident is unprecedented not only because it was one of the world’s largest cryptocurrency heists, but also because the stolen NEM was sold and money laundered on a crypto market. Three years later, the Metropolitan Police Department, Japan, announced that more than 30 people had been charged for allegedly exchanging NEM cryptocurrency, accounting for one third of the stolen value, for other cryptocurrencies. The hackers have not yet been arrested, and how the stolen NEM was money laundered has not yet been investigated. By resolving two challenges in tracking the stolen NEM and its money laundering, this report shows that there were increasingly larger sales of the stolen NEM over time, and on the last two days of market operation, approximately one third of the stolen NEM was money laundered. Furthermore, this study reveals that there was no pattern in the hour of the day of the sales transactions whereas more sales occurred on Sundays and Mondays. This suggests that the laundering was international and that the stolen NEM was purchased by individuals. These findings emphasize the need for cryptocurrency exchanges to verify the identity of a new user when an account is opened.
Raja Wasim Ahmad, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob · 5 authors
Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short in providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems is centralized, manual, and highly disintegrated which make them vulnerable to manipulation and the single point of failure problem. In this survey, we explore the potential opportunities and applications of blockchain technology in managing the exploration, production, and supply chain and logistics operations in the oil and gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss state-of-the-art blockchain-based schemes, research projects, business initiatives, and case studies to highlight the practicability of blockchain in the oil and gas industry. We present the potential opportunities brought about by blockchain technology in various use cases and application scenarios. We introduce several systems that leverage blockchain-based smart contracts to automate the important services in terms of tracking and tracing of petroleum products, protection of international trade documents, and coordination of purchasing and bidding activities for granting oil exploration rights to petroleum exploration and development companies. Finally, we present open challenges acting as future research directions.
Abstract This study analyzes the impact of a newly emerging type of anti-money laundering regulation that obligates cryptocurrency exchanges to report suspicious transactions to financial authorities. We build a theoretical model for the reporting decision structure of a private bank or cryptocurrency exchange and show that an inferior ability to detect money laundering (ML) increases the ratio of reported transactions to unreported transactions. If a representative money launderer makes an optimal portfolio choice, then this ratio increases further. Our findings suggest that cryptocurrency exchanges will exhibit more excessive reporting behavior under this regulation than private banks. We attribute this result to cryptocurrency exchanges’ inferior ML detection abilities and their proximity to the underground economy.
In this article, the author primarily aims to clarify from a technical and terminological point of view several notions relevant for the blockchain ecosystem. In this context, the analysis is focused on notions such as blockchain, Proof of Work or Proof of Stake consensus mechanism, virtual currency, crypto-asset, digital wallet, centralized or decentralized exchange platform, public address, public and private cryptographic key, seed phrase, etc. Beyond these technical and terminological clarifications, the author aims to analyse the European and national regulatory framework, in an attempt to resolve, among other things, different matters such as the difference between virtual currencies, crypto-assets and electronic money or the regulation of exchange service providers and digital wallet providers. Last but not least, the author analyses both the risks and benefits of blockchain technology from a cyber security perspective, as well as several criminal behaviours in regard to virtual currencies or other crypto-assets. In this context, behaviours such as ransomware, cryptojacking, unauthorized transfer of virtual currencies or other cryptocurrencies, counterfeiting of virtual currencies, cloning or restricting access to digital wallets, etc. are all taken into consideration.
Cryptocurrencies are a subset of virtual currencies that have been devised for anonymous payments made entirely independent of governments and traditional financial institutions. The payment system of cryptocurrencies is expanding at a rapid pace and has reached Ethiopia. This article examines the extent to which cryptocurrencies are regulated under Ethiopia’s national payment system and anti-money laundering legal norms. The study has employed doctrinal research supported by in-depth interviews. During the last decade, Ethiopia has adopted several legal frameworks that govern different aspects of the payments landscape, most notably regarding payment services and electronic money. The country has anti-money laundering legal norms that are embodied in domestic laws and international and regional instruments that it has ratified. However, these legal norms have strategic deficiencies in regulating cryptocurrencies. Thus, the government of Ethiopia should consider enacting a comprehensive law that regulates the payment system of cryptocurrencies.
The paper focuses on the links between cryptocurrencies and corruption. After providing an overview of the literature dealing with the topic, it presents an outline of possible scenarios for how cryptocurrencies can be used in corruption-tainted contracts. The scenarios imply that cryptocurrencies can reduce the costs and risks related to a corruption-tainted contract and make it easier to transfer the corruption-based benefits on an anonymous basis. Their existence also allows corruption-tainted contracts to expand to areas where this did not bring any economic advantages in the past. The paper then explores whether there are any empirical correlations between cryptocurrencies and corruption in different countries. The numbers of Bitcoin automated teller machines (ATM) and cryptocurrency users were used as a proxy for cryptocurrencies and the Corruption Perception Index (CPI) as a proxy for corruption. Although we did not find any clear relationships, we discovered that the largest number of owners or users of cryptocurrencies is in countries with a high prevalence of corruption, but the level of corruption in them did not exceed the critical limit (around the value of 30 points of the CPI index).
Abstract Terrorist financing is the economic basis of terrorist activities and the lifeline of terrorist organizations. In recent years, terrorist organizations have gradually come to use cryptocurrency to finance their activities based on traditional ways of raising funds. The anonymity of cryptocurrency is attractive to terrorist organizations, but its use remains at a low level. To explore the future development ability of cryptocurrency in terrorist financing, we study its internal characteristics and development status, as well as the supervisory systems of international organizations. This study hopes to enhance our understanding of the potential risks of cryptocurrency and serve as a reference for the fight against terrorist financing in the international community.
The aim of this paper is to present the manipulation possibilities in the operation of information technology. Many authors have already dealt with cryptocurrencies and their investment potential, with special emphasis on bitcoin. Therefore, the aim of this paper is to identify possible manipulative activities in the segment of information technology about bitcoin as a possible means of fraud in the financial market, especially if it is analysed the trend of its movement and potential financial risk. In this paper, the authors investigate in detail the characteristics of securities by linking them to market manipulations. The authors analyse bitcoin as a relative market and financial unknown, explain its origin and the most significant characteristics, and define the risks in terms of possible market manipulations. Finally, the authors analyse the financial bubble that is created around bitcoin and its impact on the economy. The authors analyse that bitcoin and other cryptocurrencies are still suitable for fraudulent activities in financial markets and emphasize the importance of institutions in reducing potential risks. Keywords: bitcoin, institutions, bubble
The prosperity of the cryptocurrency ecosystem drives the needs for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap exchanges and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the rug pull scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least $16 million from 40,165 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem.
The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms. Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX. Uniswap, as the most prominent cryptocurrency DEX, is continuing to attract scammers, with fraudulent cryptocurrencies flooding in the ecosystem. In this paper, we take the first step to detect and characterize scam tokens on Uniswap. We first collect all the transactions related to Uniswap V2 exchange and investigate the landscape of cryptocurrency trading on Uniswap from different perspectives. Then, we propose an accurate approach for flagging scam tokens on Uniswap based on a guilt-by-association heuristic and a machine-learning powered technique. We have identified over 10K scam tokens listed on Uniswap, which suggests that roughly 50% of the tokens listed on Uniswap are scam tokens. All the scam tokens and liquidity pools are created specialized for the "rug pull" scams, and some scam tokens have embedded tricks and backdoors in the smart contracts. We further observe that thousands of collusion addresses help carry out the scams in league with the scam token/pool creators. The scammers have gained a profit of at least \$16 million from 39,762 potential victims. Our observations in this paper suggest the urgency to identify and stop scams in the decentralized finance ecosystem, and our approach can act as a whistleblower that identifies scam tokens at their early stages.
The decentralization, redundancy, and pseudo-anonymity features have made permission-less public blockchain platforms attractive for adoption as technology platforms for cryptocurrencies. However, such adoption has enabled cybercriminals to exploit vulnerabilities in blockchain platforms and target the users through social engineering to carry out malicious activities. Most of the state-of-the-art techniques for detecting malicious actors depend on the transactional behavior of individual wallet addresses but do not analyze the money trails. We propose a heuristics-based approach that adds new features associated with money trails to analyze and find suspicious activities in cryptocurrency blockchains. Here, we focus only on the cyclic behavior and identify hidden patterns present in the temporal transactions graphs in a blockchain. We demonstrate our methods on the transaction data of the Ethereum blockchain. We find that malicious activities (such as Gambling, Phishing, and Money Laundering) have different cyclic patterns in Ethereum. We also identify two suspicious temporal cyclic path-based transfers in Ethereum. Our techniques may apply to other cryptocurrency blockchains with appropriate modifications adapted to the nature of the crypto-currency under investigation.