Bitcoin Transactions Research Articles

Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities

This paper proposes a unified framework for the detection of statistically significant changes in time series related to Bitcoin transactions. The time locations of these changes are linked to the occurrences of events which could be further investigated aiming to reveal potential illicit activity. The proposed framework includes: (a) the extraction of 28 features of interest in the form of time series from the Bitcoin transaction history; (b) the selection of features among the extracted ones based on the Partition Around Medoids clustering approach; and (c) the change point analysis of the multivariate time series which is formulated by the medoid time series of each cluster. This analysis enables the identification of structural breaks in the underlying behavior of the time series of interest at certain time points. The proposed framework is applied on the Bitcoin transactions of two entities that have been involved in illicit activities, namely Pirate@40, who orchestrated a high-yield investment programme, and the MintPal Bitcoin exchange platform that was hacked. The analysis results indicate that the estimated change points can be linked to certain event occurrences which may affect the transaction activity and could be further investigated for potential links to illicit actions.

Analysis of the Condition of the Development of Cryptocurrency in China

This article discusses the reasons behind the Chinese government's conservative attitude toward cryptocurrencies, despite the rapid growth of the cryptocurrency market in China. Cryptocurrencies have become popular in finance because of their advantages in privacy, permanence, and scalability. However, they also pose challenges, such as regulatory un-certainties and security risks. China has banned financial institutions from facilitating bitcoin transactions and imposed restrictions on cryptocurrencies. The article suggests that China's attitude towards cryptocurrencies may be due to their defects. Clear regulations and laws are needed to normalize transactions and issuance and mitigate policy risks. The article also provides some possible solutions based on the defects of cryptocurrencies.

A Private and Efficient Triple-Entry Accounting Protocol on Bitcoin

The ‘Big Four’ accountancy firms dominate the auditing market, auditing almost all the Financial Times Stock Exchange (FTSE) 100 companies. This leads to people having to accept auditing results even if they may be poor quality and/or for inadequate purposes. In addition, accountants may provide different auditing results with the same financial data. These issues are hard for regulators such as the Financial Reporting Council to identify because of insufficient resources or inconsistent compliance. In this paper, we proposed a triple-entry accounting protocol to allow users to report Bitcoin transactions to a third-party auditor to comply with regulations such as the travel rule. It allows the auditor to easily detect anomalies and identify the non-compliant parties, whilst the blockchain itself provides a transparent and immutable record of these anomalies. Despite building on a public ledger, our solution preserves privacy and offers an interoperability layer for information exchange. Merkle proofs were used to record non-compliant transactions whilst allowing compliant transactions to be pruned from an auditor’s active database.

Can Bitcoin be used for Zakat Payment?

Bitcoin (BTC) was developed as a digital currency to facilitate online payment transactions between one party or individual and another without using a third party. However, the use of BTC can also be misused for money laundering, fraud in investment schemes, engaging in cyber-crimes, funding activities, etc. Additionally, the highly complex method of calculating BTC makes BTC irrelevant as a measure of value. Although there are inherent advantages and disadvantages to using BTC, it can be subject to zakat. This study explores how zakat institutions can receive zakat in Bitcoin (BTC) transactions. BTC has both advantages and disadvantages, including potential misuse for illegal activities and vulnerability to security breaches. The study employs a qualitative method, including interviews with zakat institutions and BTC practitioners, and document analysis. The aim is to create a model for zakat institutions to receive donations in BTC, contributing to the management of zakat and adapting to technological changes. The study's findings can help prepare a strategy for receiving zakat in BTC and addressing potential challenges. By bridging the literature gap, the study offers new insights and perspectives on bitcoin as a zakat payment asset. Its findings can be valuable for policymakers, religious scholars, and practitioners in the realm of Islamic finance and zakat management. Ultimately, the research seeks to improve the early preparation and strategizing of zakat institutions in the face of evolving donation methods.

Bitcoin transaction fees and the decentralization of Bitcoin mining pools

In this paper, we propose a novel mechanism for decentralizing Bitcoin mining pools: the uncertain mining reward resulting from Bitcoin transaction fees. We present a simple model to demonstrate that risk-averse Bitcoin miners are more likely to allocate their computational power across multiple mining pools when transaction fees make up a large fraction of the mining reward. Our empirical study shows a negative relatinship between the proportion of transaction fees and the decentralization of Bitcoin mining pools. Our study suggests that an active Bitcoin trading market or a decreasing fixed reward from mining would lead Bitcoin mining pools to be more decentralized.

Mixing detection on Bitcoin transactions using statistical patterns

AbstractCryptocurrencies, 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 mixer‐related 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 non‐mixing 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.

Trading patterns in the bitcoin market

ABSTRACT Despite the growing literature on Bitcoin and other cryptocurrencies, we know relatively little about who are involved in trading, transacting and using these assets and how they behave. Examining millions of Bitcoin transaction records, we show that less than 1% of Bitcoin users contribute to more than 95% of the market volumes. These ‘whales’ are often associated with strategic trading/transaction volumes, market reactions and timing patterns. Using K-means clustering on a comprehensive transaction dataset, we establish a typology of traders by learning their trading exchange patterns, strategies and impact risk and market microstructure. Our approach ‘learns’ and identifies five distinct groups or types of Bitcoin users, which are somewhat, though not entirely, comparable to popular categorisations used in conventional market such as fundamental, technical, retail and institutional traders as well as market makers. Four of these groups present distinguishable trading patterns with a strong impact on liquidity provision and trading signals.

HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering

In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.

Detecting anomalous cryptocurrency transactions: An AML/CFT application of machine learning-based forensics

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

The nilcatenation problem and its application for detecting money laundering activities in cryptocurrency networks

AbstractThis work considers a combinatorial optimization problem in graphs, the nilcatenation problem, and investigates its potential application for detecting money laundering activities in cryptocurrency networks. The nilcatenation problem consists of finding a set of arcs that can be removed from an arc‐weighted directed graph without changing the balance of any vertex. The balance of a vertex is defined as the difference between the sum of the weights of outgoing and incoming arcs. We propose a 0/1 integer linear programming formulation and a local branching algorithm. The approaches are computationally evaluated and compared using three sets of test instances, two of them generated from Bitcoin's testnet and mainnet networks. An experiment on the testnet showed that it is possible to retrieve a nilcatenation artificially introduced with fake bitcoin transactions. Experiments on the mainnet showed that it is possible to find large nilcatenations, possibly indicating money laundering activities.

Illegal Community Detection in Bitcoin Transaction Networks.

Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable.

Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions

In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving unconventional side effects, rather than exchanging value. For instance, dust might be linked to on-chain services or to malicious activity, such as dust attacks attempting to break users’ pseudonymity. To study this phenomenon, this paper presents an in depth analysis of Bitcoin transactions involving dust, showing how dust is created and consumed. We identify the top dust creators and consumers and discuss how consumption has evolved over time. Finally, we use the data to identify transactions suspected of being part of dust attacks and quantify their impact on address deanonymization. Our results show that dust is mainly related to on-chain betting services. Transactions likely to be part of dust attacks are a minority of dust creating transactions but, despite their relatively low number, they have a disproportionately high effect on helping attackers to break address pseudonymity.

Bitcoin transaction pattern recognition based on semi-supervised learning

In the complex, anonymous and decentralized Bitcoin network, there exist many types of transaction patterns each of which exhibits its own unique structure. It therefore becomes very useful to analyze transaction records to recognize transaction patterns as well as the various roles that traders play in each type of transactions, which can help better understand the operations inside the network. In this paper, we propose a semi-supervised learning based method for the recognition of Bitcoin transaction patterns after defining Bitcoin transaction patterns and describing some of the existing patterns. The proposed method performs transaction pattern recognition following the clustering approach after completing several key tasks including community classification, transaction structure creation and feature construction. Results of the experiment using real data show that for known transaction patterns, the proposed method can achieve recall rate of at least 70%. Compared to some existing methods, the proposed method can achieve better recognition results by depending not only on the clustering results, but also on the recognition and clustering results of embedded data, which can enhance the effectiveness and the practicality of Bitcoin transaction pattern recognition.

A novel blockchain tumbling scalability framework in a bitcoin network

SummaryBitcoin is a decentralized digital currency system that uses a distributed data structure called blockchain, a log that lists all transaction details accomplished with the currency. The bitcoin network delivers a promising infrastructure for one who makes use of e‐currency in e‐commerce. However, the transaction of bitcoin is highly suffered with some scalability issues that could not be overwhelmed with the prior methodologies. Hence, a novel scalability blockchain tumbling framework is proposed to tackle the scalability issues in which a rake interior blockchain etiquette is designed to increase the number of bitcoin transactions in which peer faction algorithm groups blocks that reduces propagation delay and ravenous algorithm ensure higher credibility of transaction with tamper resistant. However, transaction size of the record is high hence, to manage transaction record a novel batch payment into one transaction approach is utilized to limit the size of the transaction records by initiating a precise block using block summing‐up and chagrin algorithms that collects all trust information about blocks. Thus the proposed framework attains 550 bitcoin transaction within 2 min and latency of proposed model is reduced below 60 s.

THE EFFECT OF TECHNICAL ASSET, FINANCIAL ASSET AND MACRO-ECONOMIC ON RETURN BITCOIN

This study uses research variables such as Trading Volume, Market Capitalization, S&P 500 Index, World Oil Price, Gold Price on Bitcoin Returns. The data source in this study is through internet media with the sites https://coinmarketcap.com/, https://finance.yahoo.com/, and other sites that support this research. The data used in this study are time series, and the target population for the data sample is 1,096 (3 years x 365) daily report data. The data were collected from January 1, 2020, to December 31, 2022, through the release of daily bitcoin transaction reports. Time series data analysis using Engel Granger's Error Correction Model is the method employed (ECM). The analysis tool used is the Econometric Views (Eviews) which is one of the computer econometric program. The results of this study where trading volume has a positive and insignificant effect on bitcoin returns. The impact of market capitalisation on bitcoin returns is both favorable and significant. Returns on bitcoin are positively and significantly impacted by S&P 500. The returns on bitcoin are negatively and insignificantly impacted by the price of world oil. Returns on bitcoin are positively and negligibly impacted by the gold price.

Teaching ethics in a technology regulation module: exploring the pedagogical method

ABSTRACT Technology and regulation exert significant impacts on business decision-making; as such, technology law has become an indispensable part of business education. The ethical components embedded in technology regulation modules are designed to equip future business professionals with fundamental values and rules that both guide and facilitate their decision-making. However, owing to the interdisciplinary nature of such a module and because it is frequently offered as a service module, unique instructional challenges arise. This paper identifies problem-based learning (PBL) as the main suitable pedagogical method for teaching ethical content in such a module, supplemented by scenario-based learning (SBL), because they effectively contextualise the relevant module content, create an environment that simulates real-life scenarios where moral dilemmas reside, and contribute to the module learning outcome in fostering a sustainability competency in students. Through a theoretical discussion based on concrete examples (a bitcoin transaction’s ethical issues in the environmental, social and governance (ESG) context and ethical dilemmas related to abortion rights), this paper aims to guide future studies on how to deliver ethical content within a technology law module.

BitAnalysis: A Visualization System for Bitcoin Wallet Investigation

Bitcoin is gaining ever increasing popularity. However, professional skills are required if people want to check bitcoin transaction information from the blockchain. As pointed out in a recent study, there is a lack of tools to support effective interactive investigation of bitcoin transactions. Therefore, we present a novel visualization system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> , for interactive bitcoin wallet investigation. The analytical and visualization functions of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> are defined and developed by following the advice and requirements of a group of entrepreneurs and regulators of bitcoin-related business. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> provides a rich set of functions and intuitive visual interfaces for the users, such as law-enforcement officers and regulators, to effectively visualize and analyze the transactions of a bitcoin wallet (i.e., a cluster of bitcoin addresses) and its related wallets, to track the flow of bitcoins, and to identify wallet correlation using our novel clustering functions. To achieve these functions, we have designed new visualization techniques for presenting bitcoin transactions information and introduced the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">connection diagram</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitcoin flow map</i> as new ways of analyzing, tracking and monitoring the trading activities of a cluster of closely related wallets. We also present an extensive user study that validated the effectiveness and usability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> .

Bitcoin Regulation? Imperfect Knowledge of Identities and the Money Laundering Risk: A West African Perspective

Arguments for regulating Bitcoin are built mainly on the technologically disruptive nature of the currency and its susceptibility to facilitating financial crimes on a scale larger than financial institutions. This paper questions this notion and proposes instead that the disruptive nature of Bitcoin is not technological but legal. The legal disruption requires a legislative response aimed at ensuring suitable regulation that can circumvent the identity crises in Bitcoin transactions

Asymptotically Consistent Estimation of Preferential Attachments in Growing Networks

Preferential attachment, also referred to as the “rich-get-richer” mechanism, characterizes the ability of already existing nodes in an evolutionary network to acquire new connections from newly-coming nodes throughout the growing process of the network. This mechanism is responsible for the emergence of some critical structures in many real networks, such as the Saccharomyces cerevisiae protein-protein interaction network, scientific collaboration network, and bitcoin transaction network. Clearly, it is very important to accurately estimate the preferential attachment mechanism for a growing network in interest. Although it has long been aware that the existing widely-used methods yield significantly biased estimates of preferential attachments, the underlying mechanism is not completely understood and not well explained. By rewriting preferential attachment from a deterministic formulation to a stochastic one, this paper reveals two major problems existing in the current methodologies, thereby well explaining why traditional methods introduce biases to the estimates of preferential attachments. To avoid the negative effects of these two problems, a new method is proposed based on the Poisson Pseudo Maximum Likelihood method for estimating preferential attachments, which provides asymptotically consistent estimation to preferential attachments. More importantly, even with insufficient information, the new method can still provide an estimate with high precision, showing a significant advantage in measuring preferential attachments based on small-sized samples. The new method is also robust against network parameter changes. The findings may shed some lights onto deeper understanding of the evolutionary processes of real networks.

Evolution analysis of community members for dynamic bitcoin transaction network

The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity [Formula: see text] at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network.