The âBitcoin Generator Scamâ (BGS) is a cyberattack in which scammers promise to provide victims with free cryptocurrencies in exchange for a small mining fee. In this paper, we present a data-driven system to detect, track, and analyze the BGS. It works as follows: we first formulate search queries related to BGS and use search engines to find potential instances of the scam. We then use a crawler to access these pages and a classifier to differentiate actual scam instances from benign pages. Last, we automatically monitor the BGS instances to extract the cryptocurrency addresses used in the scam. A unique feature of our system is that it proactively searches for and detects the scam pages. Thus, we can find addresses that have not yet received any transactions. Our data collection project spanned 16 months, from November 2019 to February 2021. We uncovered more than 8,000 cryptocurrency addresses directly associated with the scam, hosted on over 1,000 domains. Overall, these addresses have received around 8.7 million USD, with an average of 49.24 USD per transaction. Over 70% of the active addresses that we are capturing are detected before they receive any transactions, that is, before anyone is victimized. We also present some post-processing analysis of the dataset that we have captured to aggregate attacks that can be reasonably confidently linked to the same attacker or group. Our system is one of the first academic feeds to the APWG eCrime Exchange database. It has been actively and automatically feeding the database since November 2020.
There is growing debate over whether applications of blockchain and other financial technologies (âfintechsâ) reinforce forms of neo-colonial extraction that perpetuate NorthâSouth inequities or help enact decolonial ambitions across the Global South. This paper expands such discussions and contributes to this special issue on âfintech in Africaâ by situating emerging African blockchain techno-experimentation within wider international infrastructural relations. We argue that blockchain-based activities in and across the African continent must be understood within those also unfolding in countries that have been subjected to financial sanctions of varying types (China, Iran, Russia, Venezuela) by the European Union, United States, and United Nations. Our analysis traces how blockchain-based applications by sanctioned countries are extending exclusions in novel and existing socio-technical relations. We conclude that blockchain-based experiments are facilitating rather than displacing a colonial finance/security infrastructure.
Ardeshir Shojaeinasab, Amir Pasha Motamed, Behnam Bahrak
Abstract Cryptocurrencies, particularly Bitcoin, have garnered attention for their potential in anonymous transactions. However, their anonymity has often been compromised by deanonymization attacks. To counter this, mixing services have been introduced. While they enhance privacy, they obscure fund traceability. This study seeks to demystify transactions linked to these services, shedding light on pathways of concealed and laundered money. We propose a method to identify and classify transactions and addresses of major mixing services in Bitcoin. Unlike previous research focusing on older techniques like CoinJoin, we emphasize modern mixing services. We gathered labelled data by transacting with three prominent mixers (MixTum, Blemder, and CryptoMixer) and identified recurring patterns. Using these patterns, an algorithm was created to pinpoint mixing transactions and distinguish mixerârelated addresses. The algorithm achieved a remarkable recall rate of 100%. Given the lack of clear ground truth and the vast number of unlabelled transactions, ensuring accuracy was a challenge. However, by analyzing a set of nonâmixing transactions with our model, it was confirmed that the high recall rate was not misleading. This work provides a significant advancement in monitoring mixing transactions, presenting a valuable tool against fraud and money laundering in cryptocurrency networks.
Jinho Choi, Taehwa LEE, Kwanwoo KIM, Min-Jae Seo · 6 authors
Bitcoin is currently a hot issue worldwide, and it is expected to become a new legal tender that replaces the current currency started with El Salvador. Due to the nature of cryptocurrency, however, difficulties in tracking led to the arising of misuses and abuses. Consequently, the pain of innocent victims by exploiting these bitcoins abuse is also increasing. We propose a way to detect new signatures by applying two-fold NLP-based clustering techniques to text data of Bitcoin abuse reports received from actual victims. By clustering the reports of text data, we were able to cluster the message templates as the same campaigns. The new approach using the abuse massage template representing clustering as a signature for identifying abusers is much efficacious.
Cryptocurrency markets are unregulated, and increasingly popular, places for ordinary investors to conduct transactions, not unlike the early Wild West of the First Era discussed in Chapter One. This Chapter explores the historical origins of cryptography and its application in the digital sphere. It begins with the Cypherpunk movement against traditional institutions and their demands for transactional privacy, which led the elusive Satoshi Nakamoto to introduce a viable alternative to traditional market transaction platforms in the form of Bitcoin, a cryptocorrency powered by blockchain technology. Blockchain transaction sequences, while anonymous or, more accurately, âpseudonymous,â are self-regulated system that allow users to transact without trusting each other and without having a trusted third-party intermediary. This novel technology ultimately led to the incredible success of Bitcoin currency, which dominates the cryptocurrency market today. Undeniably, Blockchain technologies are reshaping the financial landscape, but their implications extend far beyond the financial sector alone.
Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through their criminal digital wallets. However, this is considered a paradox because cryptocurrencies are goldmines for open-source intelligence, giving law enforcement agencies more power when conducting forensic analyses. This paper proposed Inspection-L, a graph neural network (GNN) framework based on a self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN), with supervised learning algorithms, namely Random Forest (RF), to detect illicit transactions for anti-money laundering (AML). To the best of our knowledge, our proposal is the first to apply self-supervised GNNs to the problem of AML in Bitcoin. The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.
Bitcoin is electronic money that uses a public protocol to implement it in a completely decentralized fashion, eliminating the need for it to be managed by a central issuing institution. It has been demonstrated to be a modern payment system that has been utilized in some procedures frequently connected with money laundering or the trafficking of illegal substances of various kinds, although it is still under development. As a result, in this essay, we examine the characteristics that turn a cryptocurrency into a valuable tool for conducting any type of transaction outside of the supervision of any regulatory agency, as well as some of the domains in which its use can lead to new illegal activities.
It has been a decade since the formation of the first cryptocurrency in the world. Bitcoin was The first ever crypto invented by Satoshi Nag moto in 2008. It is a digital currency or virtual currency which is not controlled by the central government and operates without a central bank or single administrator. Block chain technology is used in the cryptocurrency, the cryptocurrency block chain is the chain of the blocks, where each block contains a hash of the preceding block till the top block of the chain. A network gets formed by the blocks where block chain represents a public ledger of the transaction happened in the network. Bitcoin and other cryptocurrencies have seen a rise in the attention of different sectors in the last few years. It has been in the eyes of everyone for the perks. It offers to be comparable to a fiat currency structure of banking sector, stakeholders, government and individual investors. Although research and awareness about cryptocurrencies or digital currency are very less and it is on the initial stage, this paper provides an important look and guide about the different aspects of cryptocurrency. The authenticity of this paper is on the discussion at various intervals of law and regulation with the consumption of high energy, possibility of crash collisions and security threat on the network attacks. The relative observations on future of applications of bitcoin can be seen throughout the paper.
Abstract The COVID-19 pandemic and lockdown pushed several groups of traders to rely on cryptocurrency as one of the chosen tools for commercial transactions. India is no exception. Because of its notorious misuses by criminal gangs, the Reserve Bank of India (RBI) declared cryptocurrency as derecognized. Consequently, the Indian parliament also created a draft bill titled Banning of Cryptocurrency & Regulation of Official Digital Currency Bill, 2019 (the Bill), which not only derecognizes the currency or the use of it for any commercial purposes, it also makes the investors, exchanges and agencies dealing with cryptocurrency criminally liable. Later, the Supreme Court of India in 2020 set aside the above-mentioned RBI guidelines banning cryptocurrency. But this has not nullified or suggested any amendment for the Bill. This article argues that due to this legal confusion, cryptocurrency investors, traders, exchanges and agencies, etc. have become guardian-less victims who may not be eligible to claim basic rights of victims as has been established by the United Nations Declaration of Basic Principles of Justice for Victims of Crime and Abuse of Power. In such a legal tangle, it is necessary to analyse the issues from cyber-victimological perspectives for providing functional suggestions for restitution of justice.
Combating Money laundering through cryptocurrencies has become a more challenging task due to the inherent anonymity of cryptocurrency transactions and the absence of centralized control authorities to apply known defensive laws and policies such as Know Your Customer (KYC) and Know Your Business (KYB) measures. This has led to an increase in number of cybercrimes that involve cryptocurrency as a payment method for illicit acts and a way to hide sources of dirty money. Therefore, researchers have been discovering new anti-money laundry detection and prevention techniques to combat these cybercrimes. In this work, we present an efficient anti-money laundry system that analyzes the transactions of cryptocurrency to learn data patterns that can identify licit and illicit transactions. Our system utilizes known machine learning mechanisms such as shallow neural networks and decision trees to construct the classification models. Without loss of generality, we evaluate our system on a recent bitcoin anti-money laundry dataset, the elliptic dataset, and use the classification accuracy as a performance indicator. Our analysis shows that shallow neural networks and decision trees achieve classification accuracy capped at 89.9% and 93.4%, respectively.
Manuel FebreroâBande, Wenceslao GonzĂĄlezâManteiga, Brenda Prallon, Yuri F. Saporito
This paper proposes a classification model for predicting the main activity of bitcoin addresses based on their balances. Since the balances are functions of time, we apply methods from functional data analysis; more specifically, the features of the proposed classification model are the functional principal components of the data. Classifying bitcoin addresses is a relevant problem for two main reasons: to understand the composition of the bitcoin market, and to identify addresses used for illicit activities. Although other bitcoin classifiers have been proposed, they focus primarily on network analysis rather than curve behavior. Our approach, on the other hand, does not require any network information for prediction. Furthermore, functional features have the advantage of being straightforward to build, unlike expert-built features. Results show improvement when combining functional features with scalar features, and similar accuracy for the models using those features separately, which points to the functional model being a good alternative when domain-specific knowledge is not available.
Abstract This research addresses the problems associated with hydrocarbon accounting reconciliation and allocation from the production facility to the export terminal. This paper further discusses the security of the hydrocarbon accounting database and the overall automation of the production value chain, providing transparency to the joint venture partners involved in the crude oil export agreement to avoid revenue loss. It also provides a system that is not prone to malware or data alteration and promotes hydrocarbon allocation among the injectors, production data management and production data security. The existing system mostly in the sub-saharan Africa lacks trust and greater transparency. A new technology will be devolped using Blockchain Hyperledger which is a distributed ledger technology. For the purpose of this research Javascript, PHP, MySQL on Apache virtual machine was used to design and simulate the blockchain network. The web interface of the system was tested using katalon web test framework using Agile methodology. The system will have to integrate with the Lease Automation Custody Transfer (LACT) and Supervisory Control and Data Acquisition (SCADA), A LACT-SCADANode synchronized system will be implemented in the custody transfer point in the export pipeline, so all the oil and gas Exploration and Production (E&Ps) companies injecting crude oil can view what each company in the network injected and also the quantity that got to the export terminal daily. This will be achieved by having all the custody transfer nodes of the E&Ps and export terminal node in a network of consensus. Production data will be distributed at strategic points during the transportation among the nodes uniformly at the same time, making it impossible for cyber-invasion on all the nodes at the same time. The outcome of this research will achieve an advanced secured transparent system to store production data for accurate hydrocarbon allocation, in which the consensus attribute of the implemented blockchain technology will give each node autonomy. Hence, making the production data highly reliable and reconciliation will be achieved at real-time. This innovation further presents a new knowledge in the application of mechatronics engineering to the oil and gas sector, with its multidisciplinary focus on electronics, mechanical and computer systems. It is the integration of a software system to a LACT and sensors on the crude oil pipelines to acquire data, compute in a Hyperledger fabric network and display at real-time for hydrocarbon allocation settlement
Mohammed K. Almedallah, Naif Alqahtani, Stuart D.C. Walsh
Abstract The task of managing petroleum projects is often cumbersome and complex, as these projects involve a vast number of activities that are often conducted in remote and potentially dispersed locations. In addition, petroleum projects must reconcile the many transactions that occur between multiple stakeholders, including the developer and their contractors. Due to this nature of business, projects are subject to fraudulent activity, cyber-attacks and lost time. To alleviate these issues, this paper presents a Proof-of-Work (PoW) consortium blockchain that executes and tracks oil and gas project tasks, while providing secured and transparent documentation and time stamping for all the project activities. Historically, many companies have worked on technologies to improve scheduling and project management by implementing intelligent and digitized information system solutions. Yet, these solutions require a centralized entity to monitor the activities - leading to errors and fraud. The approach adopted here overcomes these limitations by suggesting a smart contract decentralized blockchain that can solve many of the problems preventing management efficiency. The process utilizes a web-based framework that scans record tamper-evident transactions based on peer-to-peer network involving the developer and the contractor machines. The approach also creates a web interface to allow stakeholders to interact with the blockchain and store schedule updates and completion certificates. As there is no paperwork to process, and no time wasted on reconciling errors associated with filing these documents, the proposed application demonstrates a promising tool to improve speed, efficiency, and accuracy. It can enforce the contract conditions and manage the tracking of complex oil and gas activities. Employees within the developer and contractor companies can record a transaction in the network and ensure that the data within the block is not tampered with and contain a secured timestamp. The record is stored in the multiple machines available in the network and serves as a digital signature for each activity completed in the project. However, because the network is open to all stakeholders, the process requires regulation to prevent adverse events, such as members posting without restrictions. To overcome this limitation, each member is required to have a public and private key to post in the application. The paper also discusses other potential limitations to this approach to project management, and strategies that can be employed to make this use of blockchain technology viable in commercial settings. Because the data stored in the proposed blockchain is immutable, secured, and transparent, the blockchain application has the potential to transform the traditional process by tracking and digitally time-stamping all the project deliverables required by the stakeholders involved. Nevertheless, the paper argues that there are several technical, security and legal limitations needed to be addressed before having the application as mainstream.
Abstract Online markets in cryptocurrency represent a sprawling and eclectic alternative financial system, selling cutting edge techno-investment schemes that are complex and high risk. Crime control is almost entirely absent from this new crypto economy, and it is full of scams. This paper draws on an ethnography of crypto trading to review the main types of scam, suggesting that the grey economy of cryptocurrency trading is part of a wider evolution of society towards the technosocial, and beyond that perhaps towards the metaversal.
Following the rampant increase in Bitcoin prices, there has been a proliferation of cryptocurrencies, which have become a major way of doing business across national boundaries. This paper investigates the link between cryptocurrency markets and drug trafficking activities. More specifically, we explore the impact of the announcement of 24 major drug busts on the systematic risk and return of the world cryptocurrency market. We deploy an event study methodology to estimate the abnormal returns associated with drug trafficking activities in the cryptocurrency market. We find that the relationship between the two is quite strong in the case of some cryptocurrencies, albeit weaker in others. However, we show that drug bust news tends to create uncertainty, and accordingly impart risk into cryptocurrency markets. This study confirms the predictions of convenience theories of crime as to the relative attractiveness of cryptocurrencies to criminals, and the extent to which not only general, but also their own future interests, sacrificed readily on the altar of accessibility. We highlight how when social and regulatory foundations are weak, criminal behaviour may overwhelm virtual spaces, marginalizing more orthodox businesses, no matter how altruistic the intentions of their founders.
The meteoric rise of Decentralized Finance (DeFi) has been accompanied by a plethora of frequent and often financially devastating attacks on its protocols There have been over 70 exploits of DeFi protocols, with the total of lost funds amounting to approximately 1.5bn USD. In this paper, we introduce a new approach to minimizing the frequency and severity of such attacks: dissimilar redundancy for smart contracts. In a nutshell, the idea is to implement a program logic more than once, ideally using different programming languages. Then, for each implementation, the results should match before allowing the state of the blockchain to change. This is inspired by and has clear parallels to the field of avionics, where on account of the safety-critical environment, flight control systems typically feature multiple redundant implementations. We argue that the high financial stakes in DeFi protocols merit a conceptually similar approach, and we provide a novel algorithm for implementing dissimilar redundancy for smart contracts.
Abstract This article aims at investigating the extent to which the algorithmic nature (i.e., mining process) of cryptocurrencies might influence their dynamics and interaction with some major economic indicators. Our study observes that proofâofâstake based cryptocurrencies are less correlated with other cryptoâassets offering more opportunities for diversifying portfolio strategy. We also observe a positive correlation between the proofâofâwork based cryptocurrencies and the oil price. This article discusses these matters and suggests that the differences in cryptocurrencies' dynamics are more related to their service or purpose rather than their mining protocol. This claim contributes to the current debates on the intrinsic value of cryptocurrencies and it is illustrated with a discussion of the Stellar (XLM) and Ether (ETH) cases. Beyond our empirical results, our article suggests that, the liquidity and the returns dynamics of cryptocurrencies might be affected by two different aspects. Precisely, the former appears to be influenced by the economic service for which these cryptocurrencies are used, while cryptocurrencies' returns are more reactive to the way their cryptographic validation is operated. Our findings also suggest that an analysis through the economic service/purpose of cryptocurrencies is actually appropriate to understand their dynamics in relation to economic indicators. This perspective implicitly questions the monetary aspect often associated with cryptocurrencies and it calls for a more categorized research (by economic purpose) of cryptocurrencies whose potential intrinsic value would then be related to their economic purpose.
Purpose Cryptocurrencies have been used to commit various offences, but enforcement efforts remain underdeveloped relative to the value of these crimes. This paper aims to examine factors associated with outcomes of US-based cryptocurrency financial crime prosecutions. Design/methodology/approach The authors studied the 37 resolved cryptocurrency-based financial crime cases in the USA to date, exploring the impact of offence, defendant and evidence characteristics on the mode of disposition and penalties. The authors used bivariate analyses and logistic regression models to determine relationships among these variables. Findings The presence of individual defendants only (rather than a corporate defendant or combination thereof) and the use of only a cryptocurrency other than Bitcoin in committing a crime each made a case less likely to be resolved by dismissal, trial or summary or default judgement. Originality/value This paper is the first to examine variables contributing to financial crime prosecution outcomes and has implications for prosecutorial decision-making, resource allocation and the prevention and detection of financial offences involving cryptocurrencies.