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).
M. M. Fazle Rabbi, Prince Mahmud Hradoy, Md Mainul Islam, Md. Hsibul Islam · 6 authors
Blockchain technology is a decentralized ledger system that records immutable data. Nowadays blockchain is used to record, validate and secure peer-to-peer transactions. This paper focuses on how to use a blockchain network to make a partially decentralized system that we can use in banking agencies to sanction the loan. The consortium blockchain is maintained by pre-sets nodes that can improve transparency and security which is essential for the financial institution. This paper is a theoretical paper where we propose a model to validate loans and safely store the loans information in a blockchain network. The proposed model also uses the Hyperledger Fabric blockchain to implement the smart contracts method called ‘chaincodes’. The constructed system will consist of a blockchain network that can improve the reliability, efficiency, transparency, and safety of processing loans by the banking industry.
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.
Purpose This paper aims to examine the framework for the regulation of crypto assets in Germany, the UK and Switzerland focusing on anti-money laundering (AML) laws. It comprehensively addresses the risks of crypto assets and the benefits along with the changes made to the existing laws to regulate cryptocurrency. Design/methodology/approach Qualitative data was analyzed to collect information for the case study and to challenge/examine the existing data and statistics. Findings The findings suggested that the AML laws are additionally modified to include the cryptocurrencies violations of the legislation, as it is the decentralized financial systems generating opportunities for crimes and terror financing. The moderate or mild laws were found in Switzerland following Germany and the UK has the most traditional and stringent laws of money laundering. Originality/value The paper has focused on the comparison of the three states in their AML laws comprehensively along with their attitude toward the crypto businesses.
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.
Muhammad Saad, Victor Cook, Lan N. Nguyen, My T. Thai · 5 authors
Bitcoin is the leading example of a blockchain application that facilitates peer-to-peer transactions without the need for a trusted third party. This paper considers possible attacks related to the decentralized network architecture of Bitcoin. We perform a data driven study of Bitcoin and present possible attacks based on spatial and temporal characteristics of its network. Towards that, we revisit the prior work, dedicated to the study of centralization of Bitcoin nodes over the Internet, through a fine-grained analysis of network distribution, and highlight the increasing centralization of the Bitcoin network over time. As a result, we show that Bitcoin is vulnerable to spatial, temporal, spatio-temporal, and logical partitioning attacks with an increased attack feasibility due to the network dynamics. We verify our observations through data-driven analyses and simulations, and discuss the implications of each attack on the Bitcoin network. We conclude with suggested countermeasures.
Siddhartha R. Dalal, Zihe Wang, Siddhanth Sabharwal
Due to the pseudo-anonymity of the Bitcoin network, users can hide behind their bitcoin addresses that can be generated in unlimited quantity, on the fly, without any formal links between them. Thus, it is being used for payment transfer by the actors involved in ransomware and other illegal activities. The other activity we consider is related to gambling since gambling is often used for transferring illegal funds. The question addressed here is that given temporally limited graphs of Bitcoin transactions, to what extent can one identify common patterns associated with these fraudulent activities and apply them to find other ransomware actors. The problem is rather complex, given that thousands of addresses can belong to the same actor without any obvious links between them and any common pattern of behavior. The main contribution of this paper is to introduce and apply new algorithms for local clustering and supervised graph machine learning for identifying malicious actors. We show that very local subgraphs of the known such actors are sufficient to differentiate between ransomware, random and gambling actors with 85% prediction accuracy on the test data set.
A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Experimental results show that this method is significantly better than the existing smart Ponzi scheme detection methods.
One of the most modern inventions of financial technology (FinTech) since after the global financial crisis of 2008 is the crypto or virtual currency/asset. Since the creation of the first cryptocurrency, the Bitcoin, in 2009, it is estimated that over five thousand variants of the Bitcoin and other cryptocurrencies have emerged. Virtual currencies have become widespread across the globe but their legal status and uses in various countries have remained uncertain. They have been variously classified as currencies, securities, properties, assets, commodities and tokens, and used as means of exchange but are not legally recognised as legal tender. In many jurisdictions their emergence was greeted with scepticism and express or tacit rejection by financial and securities markets regulators, but over time, owing to their increasing popularity, characteristics, positive and negative potentials, there has been a gradual shift towards their formal recognition and regulation. Regulatory authorities in many countries are now grappling with designing appropriate policy and regulatory framework for the crypto phenomenon. This paper interrogates the current legal status and efforts to regulate cryptocurrencies in two leading African nations, Nigeria and South Africa, and highlights the challenges of designing an appropriate regulatory framework for this enigmatic technology. The paper adopts the doctrinal legal research methodology, employing the descriptive, analytical, and comparative approaches. It follows a structured review and analysis of relevant extant legislation on currencies and securities in the countries to ascertain whether they cover cryptocurrencies. It then compares the current position of the law on the subject in the two countries. Bearing in mind that it may not be possible to totally ban dealing in cryptocurrencies, the paper concludes that regulation has become imperative. Drawing from the position on the subject in more developed nations, the United States of America (US) and the European Union (EU), this paper proposes a model of regulation of virtual currency not only for Nigeria and South Africa but also for other African countries.
Kartick Kolachala, Ecem Simsek, Mohammed M. Ababneh, Roopa Vishwanathan
Money laundering using cryptocurrencies has become increasingly prevalent, and global and national regulatory authorities have announced plans to implement stringent anti-money laundering regulations. In this paper, we examine current anti-money laundering (AML) mechanisms in cryptocurrencies and payment networks from a technical and policy perspective, and point out practical challenges in implementing and enforcing them. We first discuss blacklisting, a recently proposed technique to combat money laundering, which seems appealing, but leaves several unanswered questions and challenges with regard to its enforcement. We then discuss payment networks and find that there are unique problems in the payment network domain that might require custom-designed AML solutions, as opposed to general cryptocurrency AML techniques. Finally, we examine the regulatory guidelines and recommendations as laid out by the global Financial Action Task Force (FATF), and the U.S. based Financial Crimes Enforcement Network (FinCEN), and find that there are several ambiguities in their interpretation and implementation. To quantify the effects of money laundering, we conduct experiments on real-world transaction datasets. Our goal in this paper is to survey the landscape of existing AML mechanisms, and focus the attention of the research community on this issue. Our findings indicate the community must endeavor to treat AML regulations and technical methods as an integral part of the systems they build and must strive to design solutions from the ground up that respect AML regulatory frameworks. We hope that this paper will serve as a point of reference for researchers that wish to build systems with AML mechanisms, and will help them understand the challenges that lie ahead.
Ehsan Rehman, Muhammad Asghar Khan, Tariq Rahim Soomro, Nasser Taleb · 6 authors
Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency.
Public blockchain records are widely studied in various aspects such as cryptocurrency abuse, anti-money-laundering, and monetary flow of businesses. However, the final blockchain records, usually available from block explorer services or querying locally stored data of blockchain nodes, do not provide abundant and dynamic event logs that are only visible from a live large-scale measurement. In this paper, we collect the network logs of three popular permissionless blockchains, that is, Bitcoin, Ethereum, and EOS. The discrepancy between observed events and the public block data is studied via a noble analysis model provided with the soundness of measurement. We share our key findings including a false universal assumption of previous mining-related studies and the block/transaction arrival characteristics.
Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launderers and terrorist organizations. This paper proposes a blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the blockchain-enabled process is used to restrict money laundering. To validate the performance of the proposed BTS method, a Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts money laundering incidents.
Evidence destruction and tempering is a time-tested tactic to protect the\npowerful perpetrators, criminals, and corrupt officials. Countries where law\nenforcing institutions and judicial system can be comprised, and evidence\ndestroyed or tampered, ordinary citizens feel disengaged with the investigation\nor prosecution process, and in some instances, intimidated due to the\nvulnerability to exposure and retribution. Using Distributed Ledger\nTechnologies (DLT), such as blockchain, as the underpinning technology, here we\npropose a conceptual model - 'EvidenceChain', through which citizens can\nanonymously upload digital evidence, having assurance that the integrity of the\nevidence will be preserved in an immutable and indestructible manner. Person\nuploading the evidence can anonymously share it with investigating authorities\nor openly with public, if coerced by the perpetrators or authorities.\nTransferring the ownership of evidence from authority to ordinary citizen, and\ncustodianship of evidence from susceptible centralized repository to an\nimmutable and indestructible distributed repository, can cause a paradigm shift\nof power that not only can minimize spoliation of evidence but human rights\nabuse too. Here the conceptual model was theoretically tested against some\nhigh-profile spoliation of evidence cases from four South Asian developing\ncountries that often rank high in global corruption index and low in human\nrights index.\n
David Sanz Bas, Carlos del Rosal, Sergio Luis Náñez Alonso, Miguel Ángel Echarte Fernández
Cryptocurrencies have been developing very rapidly in recent years, and their use is becoming more and more widespread in different areas. The use of digital currencies for legal uses is advancing along with technological development, but, at the same time, criminal activities are also emerging to take advantage of this boom. The aim of this paper has been, first, to analyze the various ways in which individuals and criminal organizations have taken advantage of the phenomenon of cryptocurrencies to carry out fraudulent activities such as laundering money of illicit origin and, second, to provide an overview of the legal tools that have been developed in this regard in Europe and, more specifically, in Spain to combat these activities. Undoubtedly, cryptocurrencies bring great benefits to the economy, but it is also necessary to know the risks and abuses that have been developed to prevent them.
The rapid growth in the number of services invites exponentially increasing malicious traffic which aims to overwhelm a server. This attempt to exhaust the server of its resources is called a Distributed Denial-of-Service (DDoS) attack. The pseudonymity (pseudo-anonymity) of Blockchain can be either desirable or undesirable to society and this unique feature put together with the elimination of trusted intermediaries that smart contracts offer, appeals to the illegal side of the world. This paper sheds light on the “dark side” of smart contracts, i.e., how they are used to stimulate criminal activity, specifically, a contract to allow the terms and execution of a DDoS to take place in a secure community. Though this proposal endeavors to release a DDoS attack, it can be extended to other real-world crimes as well. Apart from this, our work also covers how this framework could be used for Selfish Mining.
Cryptocurrency has become a new venue for money laundering. Bitcoin mixing services deliberately obfuscate the relationship between senders and recipients, making it difficult to trace suspicious money flow. We believe that the key to demystifying the bitcoin mixing services is to discover agents’ roles in the money laundering process. We propose a goal-oriented approach to modeling, discovering, and analyzing different types of roles in the agent-based business process of the bitcoin mixing scenario using historical bitcoin transaction data. It adopts the agents’ goal perspective to study the roles in the bitcoin money laundering process. Moreover, it provides a foundation to discover real-world agents’ roles in bitcoin money laundering scenarios.
Blockchain data has become a popular subject in studying various aspects of blockchains including the security of underlying mechanisms. However, the main chain block data, usually available from block explorer services, does not serve as a sufficient source of transaction and block dynamics that are only visible from a large-scale event measurement. In this paper, the transaction and block arrival events of the two popular public blockchains, i.e., Bitcoin and Ethereum, are measured to investigate the hidden dynamics of blockchain networks. We share our key findings and security implications including a false universal assumption of previous mining related studies and an invalid transaction propagation problem that can be exploited to launch a Denial-of-Service attack on a network.