As the largest blockchain platform that supports smart contracts, Ethereum has attracted wide attention from both academia and industry in recent years. Along with the prosperous development of Ethereum, the high-risk illegal practices on it are becoming more and more rampant, seriously jeopardizing the system’s trading security and long-term development. Therefore, the detection and quantification of account risk are of great importance for both cryptocurrency investors and blockchain security researchers. In this article, we propose the first general framework for account risk rating on Ethereum, which includes a devisable suspiciousness metric to adapt to various illicit fraud detection and a network propagation mechanism to formulate the relations between accounts and transactions. By conducting extensive experiments on a real-world dataset from Ethereum, we show the universality of the account risk rating framework. Particularly, statistical analyses on different risk levels of accounts demonstrate that the risk rating framework has access to detect various illicit accounts. And the metric analysis of risk rating results put forward some insights. Moreover, visualization of a suspicious transaction chain reveals the process of illicit activities on Ethereum, enabling investors to obtain an understanding of the risky accounts and avoid significant financial losses.
I combine Thorstein Veblen’s “diagnostic” approach with John R. Commons’ “remedial” approach to analyze virtual property. I focus my analysis on public blockchain based discreet assets. I conclude that the failure to fulfill two of the libertarian promises (namely, decentralized and trustless finance) does not discredit blockchain technology as such. Permissioned blockchain has promising applications. However, virtual property that is based on public blockchain facilitates extraction of value that must be politically and juridically regulated together with empowering citizens.
This paper investigates the evolution of cryptocurrencies. By nature and essence, Bitcoin challenged and implicitly threatened central bank money and its role in the monetary system. Meanwhile, central banks have been studying cryptocurrencies and launched pilot projects on their own digital currency, the Central Bank Digital Currency. Until recently, most economists considered Bitcoin merely as a speculative asset; however, the El Salvador decision in 2021 to establish it as a legal tender (through the Bitcoin Law) questions the status quo perception of Bitcoin. Given El Salvador’s legal obligation by law of their acceptance, allowing tax payments to the government and debts to be settled using Bitcoin, the Bitcoin Law challenges the boundaries of money. In light of the El Salvador experience, we consider different perspectives on the nature of money, allowing us to reject or include Bitcoin inside the money spectrum.
This paper evaluated Bitcoin financial and economic behaviour by using the econometric model on Bitcoin rate of returns compared to the alternatives assets like precious metals, stock market, and exchange rate risk. The study employed the various Quantile Regression models to observe the hedging ability of Bitcoin under bearish and bullish scenarios. The daily data of China and the USA have been collected, from July 18, 2010, to August 31, 2021. The result indicates that under different market phenomena, Bitcoin holds hedge and safe-haven asset properties against precious metals such as gold, silver, and platinum. Bitcoin can be used as an alternative to money during the currency devaluation against the US Dollar since it holds a hedge and safe- haven properties against S&P 500 Index and SSEC Index. The study elaborates the several implications for investors portfolios. Finally, the study draws the attention of policymakers towards the legalisation of Bitcoin as a currency alternative considering its efficient performance under different economic conditions, supported by detailed theoretical and empirical analyses.
The use of blockchains for automated and adversarial trading has become commonplace. However, due to the transparent nature of blockchains, an adversary is able to observe any pending, not-yet-mined transactions, along with their execution logic. This transparency further enables a new type of adversary, which copies and front-runs profitable pending transactions in real-time, yielding significant financial gains. Shedding light on such "copy-paste" malpractice, this paper introduces the Blockchain Imitation Game and proposes a generalized imitation attack methodology called Ape. Leveraging dynamic program analysis techniques, Ape supports the automatic synthesis of adversarial smart contracts. Over a timeframe of one year (1st of August, 2021 to 31st of July, 2022), Ape could have yielded 148.96M USD in profit on Ethereum, and 42.70M USD on BNB Smart Chain (BSC). Not only as a malicious attack, we further show the potential of transaction and contract imitation as a defensive strategy. Within one year, we find that Ape could have successfully imitated 13 and 22 known Decentralized Finance (DeFi) attacks on Ethereum and BSC, respectively. Our findings suggest that blockchain validators can imitate attacks in real-time to prevent intrusions in DeFi.
We have been hearing about Bitcoins for several years not only in the news but also in TV series. The problem is that in most cases, especially in TV series, what Bitcoins really are and what we can do with them is distorted. It is not controlled by any authority, it is not stored in banks, it is not traceable and, in many cases, especially in the early days, it is associated with illegal activities related to drug and arms trade. But if we dig a little deeper into what this new currency actually means, we can see that it could become a widely used currency by users in the near future. Bitcoins is the thing that used by any person or organization without any restriction and this currency may be used as a currency officially. To understand transferring bitcoins among parties globally, it can be essential to clarify what a bitcoin is itself.
As the largest blockchain platform that supports smart contracts, Ethereum has developed with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum has fostered the emergence of various illegal activities and money laundering by converting ill-gotten funds to cash. In the traditional money laundering scenario, researchers have uncovered the prevalent traits of money laundering. However, since money laundering on Ethereum is an emerging means, little is known about money laundering on Ethereum. To fill the gap, in this paper, we conduct an in-depth study on Ethereum money laundering networks through the lens of a representative security event on \textit{Upbit Exchange} to explore whether money laundering on Ethereum has traditional traits. Specifically, we construct a money laundering network on Ethereum by crawling the transaction records of \textit{Upbit Hack}. Then, we present five questions based on the traditional traits of money laundering networks. By leveraging network analysis, we characterize the money laundering network on Ethereum and answer these questions. In the end, we summarize the findings of money laundering networks on Ethereum, which lay the groundwork for money laundering detection on Ethereum.
This research develops a methodology to identify transactions through data-driven tracking and analysis of ransomware-Bitcoin payment networks [30]. We demonstrate the methodology by applying the GraphSAGE embedding algorithm to the WannaCry ransomware-Bitcoin cash-out network. The paper takes a data-driven approach to building a machine learning system that allows analysts to define features relevant to ransomware-Bitcoin payment networks.
In recent times, there has been a swift advancement in the field of cryptocurrency. The advent of cryptocurrency has provided us with convenience and prosperity, but has also given rise to certain illicit and unlawful activities. Unlike classical currency, cryptocurrency conceals the activities of criminals and exposes their behavioral patterns, allowing us to determine whether present cryptocurrency transactions are legitimate by analyzing their behavioral patterns. There are two issues to consider when determining whether cryptocurrency transactions are legitimate. One is that most cryptocurrency transactions comply with laws and regulations, but only a small portion of them are used for illegal activities, which is related to the sample imbalance problem. The other issue concerns the excessive volume of data, and there are some unknown illegal transactions, so the data set contains an abundance of unlabeled data. As a result, it is critical to accurately distinguish between which transactions among the plethora of cryptocurrency transactions are legitimate and which are illegal. This presents quite a difficult challenge. Consequently, this paper combines mutual information and self-supervised learning to create a self-supervised model on the basis of mutual information that is used to improve the massive amount of untagged data that exist in the data set. Simultaneously, by merging the conventional cross-entropy loss function with mutual information, a novel loss function is created. It is employed to address the issue of sample imbalance in data sets. The F1-Score results obtained from our experimentation demonstrate that the novel loss function in the GCN method improves the performance of cryptocurrency illegal behavior detection by four points compared with the traditional loss function of cross-entropy; use of the self-supervised network that relies on mutual information improves the performance by three points compared with the original GCN method; using both together improves the performance by six points.
Shyam Mehta, K. Shantha Kumari, Paras Jain, Harshal Raikwar · 5 authors
When a cognizable violation like murder, abduction, rape, theft, etc. is committed, a victim or someone acting on their behalf must submit an electronic first information report (e-FIR) to the police station. Due to the centralized nature of the e-FIR database, it is possible for the offense’s record to be hacked, and it is also possible for fake e-FIRs to be purposefully registered. Data transparency and integrity are therefore major issues with the e-FIR database. Indian government launched nation-wide Crime and Criminal Tracking Network and Systems (CCTNS) during 2009 and it is an efficient e-governance system. This paper provides a blockchain solution to handle the complaints given on both cognizable and non-cognizable complaints. Using real-world examples from previous events, the technology and security underlying the use of blockchain will be discussed. Police will file an e-FIR, which will be validated by the authorities, and on acceptance of the FIR, it will be encrypted and stored as a hash along with the timestamp and hash of the next block. Now, how blockchain makes it secure is that it doesn’t let anybody make changes to the FIR without proof of work and a vote of consensus in which a majority of the blockchain must agree to the change. The hash will be stored in smart contracts using Ethereum. Our findings demonstrate a trade-off between the number of transactions contained in a single block on the blockchain ledger and the security level of various hashing algorithms for the offence data.
Cryptocurrencies, first introduced in 2009, have rapidly grown in value, volume, and use. The sentiment that they are no more than a passing fad and need not be dealt with by scholars or policymakers remains but is no longer legitimate. Hostile actors such as terrorist groups and sanctioned states use cryptocurrencies to finance activities ranging from deadly attacks to rogue nuclear programs. The use of cryptocurrencies by legitimate actors brings far less dramatic externalities but still presents serious issues, such as the vulnerability of citizens to cryptocurrency-related scams, lost tax revenue, and the stress cryptocurrencies place on countries' electrical grids. This chapter provides readers with an understanding of exactly how cryptocurrencies function, how hostile actors use them, and how governments respond so that they may better understand the challenges posed by cryptocurrencies and how they might be addressed.
Transactions on the darknet are notoriously difficult to examine. Prior criminological research has generally used web scraping and qualitative text analysis to examine illegal darknet markets. One disadvantage of this process is that individuals can lie. Fortunately for researchers, the currency used for transactions on the darknet, cryptocurrency, is designed to be tracked. In this article, we examine transactions from a former darknet marketplace, AlphaBay. Using the blockchain, we examine the interconnectedness of both legal and illegal cryptocurrencies. In addition, we provide a structured approach to quantitatively examine the Bitcoin blockchain ledger, offering both the tools and our own experiences for other researchers interested in such approaches. While cybersecurity, information technology, accounting, and other disciplines can examine the financial data itself, we believe that criminologists can provide additional benefits in pattern analysis and organizing the context and theory around the transactions. Our results show that cryptocurrency transactions are generally identifiable (90%) and involve likely illegal transactions, transactions that attempt to obfuscate other transactions, and legal transactions. We end with a discussion of newer cryptocurrencies and related technology and how they will likely shape future work.
Decentralized autonomous organizations (DAOs) are blockchain-based organizations fed by a peer-to-peer (P2P) networkof contributors. Their management is decentralized without top executive teams and built on automated rules encoded insmart
Fake news, misinformation and disinformation have significantly increased over the past years, and they have a profound effect on societies and supply chains. This paper examines the relationship of information risks with supply chain disruptions and proposes blockchain applications and strategies to mitigate and manage them. We critically review the literature of SCRM and SCRES and find that information flows and risks are relatively attracting less attention. We contribute by suggesting that information integrates other flows, processes and operations, and it is an overarching theme that is essential in every part of the supply chain. Based on related studies we create a theoretical framework that incorporates fake news, misinformation and disinformation. To our knowledge, this is a first attempt to combine types of misleading information and SCRM/SCRES. We find that fake news, misinformation and disinformation can be amplified and cause larger supply chain disruptions, especially when they are exogenous and intentional. Finally, we present both theoretical and practical applications of blockchain technology to supply chain and find support that blockchain can actually advance risk management and resilience of supply chains. Cooperation and information sharing are effective strategies.
Due to a complexity of Bitcoin transaction graph and a class-imbalance difficulty as only a few known labels of blockchain illicit activities exist, graph convolutional network (GCN) using focal loss constraints is applied in this paper for anti-money laundering detection. The GCN, composed of spectral graph convolution layers suitably handles graph structured input data; whereas the focal loss (FL) has been applied to resolve the severe problem of class-imbalance by reducing the loss weight of easy-to-classify samples. In addition, the licit and illicit detection performance comparison on Elliptic dataset among 2, 4 and 6 numbers of GCN layer (GCN -2L, -4L and -6L) relying upon focal loss relative to cross-entropy (CE) loss is assessed. Assessment criteria depend on precision, recall, F1 and accuracy scores, averaged through 10-fold cross validation. A Bitcoin transaction graph, so-called Elliptic dataset derived from Bitcoin blockchain is experimented to detect illicit transactions. The results indicate insignificant difference of accuracy rate between GCN -2L, -4L and -6L models using FL and those employing CE. However, apparently and interestingly, illicit transactions are 4.78%, 5% and 6.20% more correctly predicted by using FL than CE, consecutively relying on GCN -2L, 4L and 6L. Illicit transactions are 4.94%, 5.31% and 7.84% less misclassified by FL than CE.
Lakshmi P. Krishnan, Iman Vakilinia, Sandeep Reddivari, Sanjay Ahuja
With the emergence of cryptocurrencies and Blockchain technology, the financial sector is turning its gaze toward this latest wave. The use of cryptocurrencies is becoming very common for multiple services. Food chains, network service providers, tech companies, grocery stores, and so many other services accept cryptocurrency as a mode of payment and give several incentives for people who pay using them. Despite this tremendous success, cryptocurrencies have opened the door to fraudulent activities such as Ponzi schemes, HYIPs (high-yield investment programs), money laundering, and much more, which has led to the loss of several millions of dollars. Over the decade, solutions using several machine learning algorithms have been proposed to detect these felonious activities. The objective of this paper is to survey these models, the datasets used, and the underlying technology. This study will identify highly efficient models, evaluate their performances, and compile the extracted features, which can serve as a benchmark for future research. Fraudulent activities and their characteristics have been exposed in this survey. We have identified the gaps in the existing models and propose improvement ideas that can detect scams early.
Fraud across the decentralized finance (DeFi) ecosystem is growing, with victims losing billions to DeFi scams every year. However, there is a disconnect between the reported value of these scams and associated legal prosecutions. We use open-source investigative tools to (1) investigate potential frauds involving Ethereum tokens using on-chain data and token smart contract analysis, and (2) investigate the ways proceeds from these scams were subsequently laundered. The analysis enabled us to (1) uncover transaction-based evidence of several rug pull and pump-and-dump schemes, and (2) identify their perpetrators’ money laundering tactics and cash-out methods. The rug pulls were less sophisticated than anticipated, money laundering techniques were also rudimentary and many funds ended up at centralized exchanges. This study demonstrates how open-source investigative tools can extract transaction-based evidence that could be used in a court of law to prosecute DeFi frauds. Additionally, we investigate how these funds are subsequently laundered.
Despite the rise in Bitcoin scams, there is a lack of thorough research on the subject. Sophisticated scammers and money launderers can take advantage of Bitcoin mixing services and exchanges. Bitcoin mixing services will send more coins to the customer's address after deducting the mixing cost. The frequency and amount of payments/fees may seem unpredictable, but this is the reality. Criminals use bitcoin mixing services to disguise the source of lost profits and pay securely through cryptocurrency exchanges for anonymous use. Bitcoin's normative unpredictability and organizational opacity attract people with shady intentions, such as supporting rebels or buying drugs. The results of this scoping study show that investigations into cryptocurrency fraud are increasing in scope and depth, but are in the early stages of examining future concerns and circumstances.
Cryptocurrency is the form of currency that resides digitally or virtually. The transaction can be secured by using cryptography. Since the decentralized system is applied to record the transactions the cryptocurrency does not have a central issuing or regulating authority. Besides, it explains the perception of blockchain technology. Among the several metrics in the market cap, cryptocurrency endures as the number one digital currency in the world. In the past decade, with novelty and a collective market capitalization the size of the cryptocurrency space has grown exponentially. Cryptocurrencies desire determined and secure mining methods. In this study, we survey and analysed the current mining. In this study, we survey and compare the current mining approach applied to the extensive Cryptocurrencies. We assess the strengths, deficiencies, and viable threats to each mining approach. On the whole, corresponding efficacy, assurance and the site show the exceptional threats and strengths are delineated.
Nhung Nguyen, An Tuan Nguyen, Ha Thi Nguyet To, Thanh Ta Hong Le
Purpose This paper aims to explore factors influencing Vietnamese people’s susceptibility to fraud through cryptocurrency Ponzi schemes. Design/methodology/approach This study uses the gullibility theory, the theory of planned behavior theory, the traditional theory of finance and the theory of financial behavior, to develop a questionnaire which is then sent to respondents who are Vietnamese individuals. Subsequently, the partial least squares structural equation modeling approach (PLS-SEM) is used to analyze 370 collected responses. Findings This research shows the important roles that trust, risk appetite and knowledge of Ponzi play in respect of fraud susceptibility, among which trust has the highest positive impact. Moreover, there is no evidence of relationships between Vietnamese people’s susceptibility to fraud via cryptocurrency Ponzi schemes and attitudes toward investment scams, knowledge of investment or knowledge of Ponzi schemes. Research limitations/implications This paper collects only 370 valid responses, which raises some questions regarding the diversity and representativeness of the survey sample. Practical implications This study provides evidence on factors affecting Vietnamese people’s fraud susceptibility to cryptocurrency Ponzi schemes, which helps both authorities and individuals to be vigilant against investment scams. Social implications This research proposes several recommendations to prevent investment scams in cryptocurrency trading, from the perspective of state regulators and individuals. Originality/value This working paper provides a new approach using PLS-SEM to build a theoretical framework for the possibility of becoming victims of investment scams in Vietnam using a combination of different theories from criminology and finance.
This chapter provides an overview of crimes involving cryptocurrencies. It first introduces the concepts of crypto-enabled and crypto-dependent crimes, which offer a framework for categorizing the types of crime. The chapter also describes the characteristics of cryptocurrencies which may facilitate their use by criminals. It then gives an overview of the various types of crime that involve cryptocurrencies, including types of fraud, cyber-crimes, cryptocurrency mining crimes, money laundering, terrorism financing, sanctions evasion, tax evasion, darknet marketplaces, bribery and corruption, and cryptocurrency-adjacent crimes. Finally, the chapter considers crimes associated with particular areas of the larger cryptocurrency ecosystem, namely, decentralized finance (DeFi) and non-fungible tokens (NFTs), and offer conclusions about cryptocurrencies and criminal activity. Many DeFi projects and protocols are governed in a decentralized manner through decentralized autonomous organizations. NFTs are digital records of ownership and authenticity of some asset (which may be digital or physical) created by smart contracts and stored on a blockchain.
Sadia Nazar Hussain, Abdul Raheman, Muhammad Anwar ul Haq
Water always finds its way" so do the money launderers, who are always successful in finding new ways of committing the crime. This study primarily aims to identify opportunities that launderers are exploiting to whitewash their black money. Dual nationality (DN), financial system sophistication (FSS), and cryptocurrency legal status (CCLS) are the advanced opportunities being used by launders to clean their funds. Some studies highlight the link between cryptocurrency and money laundering, but the role of dual nationality and financial system sophistication in money laundering is still a less addressed phenomenon (Ebeke, 2011) and (T. P. and J. Walker, 2011). The objective of this study is to explore the role of these three variables in money laundering by making improvements to the original version of the Walker model. The study's theoretical model was developed by borrowing justifications from Rational Choice Theory (RCT). For quantitative analysis, FGLS was employed over strongly balanced panel datasets. The final dataset of the study was prepared by gathering secondary data from 177 countries for 11 years (2009-2019). The study has found that overall, financial system sophistication is an important factor in choosing a laundering centre around the globe. However, Pakistani launderers do not perceive FSS as an attractive element for laundering their black money. In contrast, dual nationality was identified as a significant element in money laundering from Pakistan to other countries. However, the aspect of cryptocurrency legal status was found to be a significant attractive element for both national and international launders. The findings of the study guide policymakers and practitioners in strengthening the anti-money laundering strategy.
The banking sector is identified as the main means for laundering illicit money, these banks generally have access to both banking mechanism and legal authority to make decisions. Money launderers and those financing terrorism are conveniently accessing financial institutions and its mechanism. These institutions provide all financial funds transfers both domestic and international range. Anti-money laundering (AML) laws and other data protection laws that keep getting stricter have forced many financial institutions to put in place long, expensive processes to stay in compliance. To bridge the gap, emerging technology can help in mitigating money laundering and other financial crimes. Blockchain is considered one of the world's best-known examples of Distributed Ledger Technology. In the financial sector, this type of technology has been hailed as the key to future success. Emerging technology can be used in many ways in Financial Services. It can change many processes, payments between peer-to-peer, trade agreements and tracking of supply chains. Emerging technology can be used in many ways in financial services and can change many processes, peer-to-peer payments, trade agreements, and the tracking of supply chains. These use cases depend on the participants or users being identified and verified. "Know Your Customer" is the term for this (KYC). Before making a transaction, one of the most basic ways to build trust between the people involved is to check out the user. This current paper focuses on issues of compliance and Anti Money Laundering policies in the banking sector by using new emerging technologies such as blockchain; further, the paper focuses keenly on issues relating to the manipulation of KYC and the financial burden on banks while also addressing AML policies, Finally, the paper provides ideas and suggestions regarding the rise of emerging technologies such as blockchain while addressing the problems of ML. This also includes the capability of blockchain technology to bring banking systems with recalibrated mode of compliances polices.