Daniel Mider, PrzemysĹaw Potocki, Robert Staniszewski
PURPOSE: The aim of this article is to examine the volume, trust and risk perceived by Poles in terms of broadly understood types of payments, from cash, through various electronic payments, to cryptocurrencies. This is particularly important in the context of the hypothesis of a cashless societies predicted by some researchers.
Hanna Kim, Jian Cui, Eugene Jang, Chanhee Lee ¡ 7 authors
As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year, \$100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design \textit{DRAINCLoG}, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of \textit{DRAINCLoG} under a wide variety of evasion attacks.
Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current-related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multiconditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3% more one-time change addresses than other heuristics. On top of the multi-input heuristic clustering method, the proposed method also improves the address clustering performance by 5.7%, achieves optimal recognition results compared with similar methods, and significantly reduces the false positive rate of recognition results. This work provides the technical basis for antimoney laundering efforts based on entity identification. Code and data could be accessed from https://github.com/ECNU-Cross-Innovation-Lab/BitcoinAddressClustering.
Abstract As a financial innovation of the information age, cryptocurrency is a complex concept with clear advantages and disadvantages and is worthy of discussion. Exploring from a terrorism perspective, this study uses the time-varying parameter/stochastic volatility vector autoregression model to explore the risk hedging and terrorist financing capabilities of Bitcoin. Empirical results show that both terrorist incidents and brutality may explain Bitcoin price, but their effects are slightly different. Compared to terrorist brutality, terrorist incidents have a weaker impact on Bitcoin price, showing that Bitcoin investors are more concerned about the number of deaths than the frequency of terrorist attacks. In turn, the impact of Bitcoin price on terrorist attacks is negligible. Bitcoin is a potential means of financing terrorism, but it does not currently play an important role. Our research findings can help investors analyze and predict Bitcoin prices and help improve the theoretical system of anti-terrorist financing, helping to maintain world peace and security.
The concept of Bitcoin first came into creation in 2008, as a response to the Great Financial Crisis, and the world of finances relies upon banks for all kinds of financial transactions as intermediaries. Bitcoin is more than a cryptocurrency used as payment for investors to hold and hope for value; it is considered a digital asset these days. While Bitcoin transactions take place in the cyber network, possibilities are there for cyber-attacks. To protect Bitcoins from illegal miners of Bitcoin data, it is required to find all possible intrusions, called vulnerabilities. If more vulnerabilities exist in the network that makes the transactions vulnerable. As of now, Bitcoin uses centralized Blockchain technology, where the transaction made by the user are managed by various blockchain methods like Merkelroot, Mining, Halving, Keys, and Wallets. These methods provide security to the cryptocurrencies online. Among all the four methods, Wallets are somewhat vulnerable due to their dual services i.e., custodians and non-custodians. Non-custodian Wallet type doesnât cause vulnerability due to not having any storage for keys. Whereas the Custodian Wallet type provides storage of keys for the customers which makes it as vulnerable. The techniques that lead to vulnerability in the custodian type are hot storage, cold storage, and deep cold storage. The exploitation of all these individual techniques is the main task of this work. When it comes to transactions of bitcoin along the network where the receiver needs to wait around 30 minutes due to mempool. This is also a loophole that may trigger the threat to the bitcoin exchange. These vulnerabilities can be reduced by regulatory oversight and reducing the mempool waiting time or it is possible by maintaining more legal crypto miners for every block of transaction. To verify the whole process of bitcoin transaction exchanges, few cloud services use blockchain technology for processing transactions. The proposed work is to implement the above methods, so that protection is provided to cryptocurrencies toward bitcoin exchanges.
Anton Wahrstätter, JorĂŁo Gomes, Sajjad Khan, Davor SvetinoviÄ
The potential of Bitcoin for money laundering and terrorist financing represents a significant challenge in law enforcement. In recent years, the use of privacy-improving CoinJoin transactions has grown significantly and helped criminal actors obfuscate Bitcoin money flows. In this study, we use unsupervised machine learning to analyze the complete Bitcoin user graph in order to identify suspicious actors potentially involved in illegal activities. In contrast to the existing studies, we introduce a novel set of features that we use to identify potential criminal activity more accurately. Furthermore, we apply our clustering algorithm to a CoinJoin-adjusted variant of the Bitcoin user graph, which enables us to analyze the network at a more detailed, user-centric level while still offering opportunities to address advanced privacy-enhancing techniques at a later stage. By comparing the results with our ground truth data set, we find that our improved clustering method is able to capture significantly more illicit activity within the most suspicious clusters. Finally, we find that users associated with illegal activities commonly have significant short paths to CoinJoin wallets and show tendencies toward outlier behavior. Our results have potential contributions to anti-money laundering efforts and combating the financing of terrorism and other illegal activities.
With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose Asset Transfer Paths and corresponding path graphs to characterize early transaction patterns. Furthermore, since transaction patterns change rapidly in the early stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode asset transfer path and path graph under an evolving structure setting. Hierarchical Survival Predictor then predicts addresses' labels with high scalability and efficiency. We investigate the effectiveness and generalizability of Evolve Path Tracer on three real-world malicious address datasets. Our experimental results demonstrate that Evolve Path Tracer outperforms the state-of-the-art methods. Extensive scalability experiments demonstrate the model's adaptivity under a dynamic prediction setting.
Raffaele Trequattrini, Matteo Palmaccio, Mario Turco, Alberto Manzari
Abstract This paper proposes a systematic literature review emphasising scholars' view on the impact of the adoption of blockchain technologies to fight corruption. Providing a deep understanding of the state of the art, the paper drafts implications and valuable insights to address future research. The connection of anticorruption policies and blockchain technologies has been investigated using Scopus database and ABS journal ranking. The importance assumed by anticorruption policies and the innovativeness of the blockchain technology, thanks to its characteristics of immutability, automatic timestamping and distributed architecture, needs to be investigated in an integrated perspective. This study provides for the first time a systematic connection among anticorruption practices and blockchain towards the lens of the business, management and accounting field, providing valuable implications, insights and emerging issues.
Mohammed Al Shamsi, Deborah Drummond Smith, Kimberly C. Gleason
Purpose The purpose of this paper is to describe how non-fungible tokens (NFTs) can be used in the commission of financial crime, including money laundering and crypto-fraud schemes, using the framework of the Space Transition Theory. Design/methodology/approach A literature review relating the Space Transition Theory to crime vulnerabilities related to NFTs is conducted and practical examples illustrating NFT schemes are provided. Findings The authors find that the Space Transition Theory explains the evolution of financial crimes into the NFT space. The transformation of the art industry from the physical to the virtual space through NFTs underlies the criminal activity surrounding them. NFTs enable crime because of the flexibility, dissociative anonymity, lack of deterrence and anonymity. Research limitations/implications Criminals can easily take advantage of the usersâ limited knowledge of blockchain to defraud them of their money or tokens. These risks accentuate the need to adopt appropriate measures to augment the accountability of NFT transactions. Until such interventions are implemented, the NFT market remains a highly viable space for the perpetration of financial crimes. Practical implications The dynamic nature of the cyberspace and fast-past underlying technology provide a greater chance to escape than crimes committed in the physical space. The state of security on NFT platforms has elicited concerns from diverse quotas. NFTs pose significant money laundering risks because of the lack of appropriate regulatory mechanisms, generating a need for enhanced oversight and enforcement of sectors of the economy in physical space vulnerable to abuse in the NFT space, including entities such as art galleries, museums, sports teams and luxury brands. Social implications The Space Transition Theory is also supported in that norms and values regarding ethics and criminal actions in the physical space do not transfer to cyber space. Originality/value The novelty aspect of this research is in applying the Space Transition Theory to financial crime schemes based on NFTs.
Cryptoassets have recently attracted the attention of national and international financial regulators. Since the mid-2010s blockchains have increasingly been adapted to automate and replace many aspects of financial intermediation, and by 2015 Ethereum had created the smart contract language that underpins the digitization of real assets as asset-backed tokens (ABTs). Those were initially issued by FinTech companies, but more recently banks active on international capital and financial markets, and even central banks, for example, the Bank of Thailand, have developed their own digital platforms and blockchains. A wide variety of real and financial assets underpins ABTs, viz., real-estate, art, corporate and sovereign bonds, and equity. Consequently, owing to the significant market capitalization of cryptocurrencies, the Basel Committee on Banking Supervision (BCBS) published two consultative papers delineating its approach on cryptoasset regulation. In this study, the authors analyze the mechanics of ABTs and their potential risks, relying on case studies of recent issuance of tokens in equity, real-estate, and debt markets, to highlight their main characteristics. The authors also investigate the consequences of the increasingly oligopolistic structure of blockchain mining pools and Bitcoin exchanges for the integrity and security of unregulated distributed ledgers. Finally, the authors analyze the BCBSâ regulatory proposals, and discuss the reaction of international financial institutions and cryptocurrency interest groups. The main findings are, firstly, that most ABTs are akin to asset-backed securities. Secondly, nearly all ABTs are âoff-chain/on-chain,â that is, the underlying is a traditional asset that exists off-chain and is subsequently digitized. The main exception is the World Bankâs bond-i that is genuinely native to the blockchain created by the Commonwealth Bank of Australia, and has no existence outside it. Thirdly, all ABTs are issued on permissioned blockchains, where anti-money laundering/anti-terrorist funding and know-your-customer regulations are enforced. From a prudential regulatory perspective, ABTs do not appear to pose serious systemic risks to international financial markets. This may account for the often negative reactions of banks, banking associations, and cryptocurrency interest groups to the BCBSâ 2021 proposals for risk-weighted capital provisions for cryptoassets, which are viewed as excessive. Finally, we found that issuance of ABTS and other smart contracts on permissionless blockchains such as Bitcoin and Ethereum could potentially generate financial instability. A precedent involving Ethereum and The DAO in 2016 shows that (i) there is a significant accountability gap in permissionless blockchains, and (ii) the core developers of blockchains and smart contract technology, and Bitcoin mining pools, exercise an unexpectedly high- and completely unregulated-amount of power in what is supposedly a decentralized network.
Blockchain-based cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), are newly emerging financial assets. Cryptocurrency exchanges are marketplaces for cryptocurrency circulation while becoming a new venue for money laundering. In this work, we cooperate with a cryptocurrency exchange to investigate new solutions for anti-money laundering in cryptocurrency exchanges. First, we learn the domain knowledge of cryptocurrency transactions and summarize data analytical requirements of transaction supervisors in their daily work of anti-money laundering. Then, we propose a visual analysis approach to support their daily work. The approach consists of a new algorithm that automatically detects suspicious money laundering accounts and a multiviewed user interface that visualizes the algorithm results and relevant transaction data. An abacus-inspired visualization is designed in the interface to depict transaction patterns contained in numerous cryptocurrency transactions, which can help supervisors find money laundering clues and deduce the trading tactic adopted by launderers. Finally, an algorithm performance experiment, a case study, and a field study are conducted with real-world data to demonstrate the effectiveness of our solution.
Jan 1, 2023¡Proceedings of the 3rd International Conference on Law, Social Science, Economics, and Education, ICLSSEE 2023, 6 May 2023, Salatiga, Central Java, Indonesia
Virtual money or cryptocurrency has become a hot topic in electronic media discussions lately. Cryptocurrency is a form of intangible commerce that only exists in digital form and can be used in electronic transactions. This study aims to demonstrate that the Act Number 8 of 2010 concerning the Prev
The traditional currency cross-border circulation model has many problems in terms of efficiency, cost, and security.The private digital currencies represented by Bitcoin and Ethereum are flourishing and occupying an increasingly important position in cross-border payments.However, digital currencies themselves face great uncertainty and affect the stable operation of the global monetary system.In response, countries have started to develop CBDCs to enhance their financial inclusion and international competitiveness of their currencies with the advantages of digital currencies.However, the birth of new technologies often creates new problems, and CBDCs face a trade-off between privacy and transparency in their design, which can directly affect the supervision of CBDC funds and lead to CBDCs becoming a tool for financial crimes.This paper introduces the background and dynamics of CBDC development and the challenges it faces and combines existing relevant research and literature to propose countermeasures against CBDC financial crimes from several aspects: big data, artificial intelligence, and new regulation.Finally, future research directions in this field are proposed.
The purpose of this paper is to suggest the uses of a simulator to evaluate how vulnerable is a cryptocurrency. It is called âThe Cryptocurrency Vulnerability Evaluator (CV-Evaluator)â. The main objective of the CV-Evaluator is to evaluate how vulnerable is a cryptocurrency in case of a deep financial crisis, war, inflation, unemployment, economic desgrowth, corruption, large trade and government deficit, natural disasters, or pandemic. Hence, our final target is to observe WHEN to buy or sell cryptocurrencies (Angel & McCabe, 2015) in the right timing under different circumstances and environments. It is based on the uses of different levels of risk through the visualization of a cube that keep in constant movement and warp stage all the time. Finally, we are using Bitcoin such as an experimental laboratory to test our simulator.
This bachelor's thesis revolves around the acceptance of Bitcoin as legal tender in El Salvador and the subsequent reactions from the international community, particularly the United States and the International Monetary Fund. Using the case of El Salvador as a study, the thesis seeks to explore a broader phenomenon concerning Latin America and the potential for Bitcoin to be the key for positive socio-economic transformation in the region. The theoretical part of the thesis delves into the concepts of dollarization and the governance of Bitcoin, in addition to the world-systems theory proposed by Immanuel Wallerstein. Subsequently, the thesis applies these theories to concrete facts and conducts an analysis. The timeframe for this research spans from September 2021, when Bitcoin was officially adopted as legal tender in El Salvador, until the end of 2022.