Bitcoin Transactions Research Articles

Abnormal Trading Visualized Detection on Bitcoin Transaction Based on Semi-Supervised Machine Learning and Graph Database

Abnormal Trading Visualized Detection on Bitcoin Transaction Based on Semi-Supervised Machine Learning and Graph Database

Poisson-Lindley minification INAR process with application to financial data

<p>This paper introduces the Poisson-Lindley minification integer-valued autoregressive (PL-MINAR) process, a novel statistical model for analyzing count time series data. The modified negative binomial thinning and the Poisson-Lindley (PL) marginal distribution served as the foundation for the model. The proposed model was examined in terms of its basic stochastic properties, especially related to conditional stochastic measures (e.g., transition probabilities, conditional mean and variance, autocorrelation function). Through comprehensive simulations, the effectiveness of various parameter estimation techniques was validated. The PL-MINAR model's practical utility was demonstrated in analyzing the number of Bitcoin transactions and stock trades, showing its superior or comparable performance to the established INAR model. By offering a robust tool for financial time series analysis, this research holds potential for significant improvements in forecasting and understanding market dynamics.</p>

EXTENDING THE SPV CONTRACT CONCEPT WITH PRIVACY GADGETS

The SPV (Simple Payment Verification) contract extends the functionality of Bitcoin by enabling cross-chain operations through trustless synchronization with other blockchain systems. This approach allows users to verify Bitcoin transactions without running a full node by utilizing block headers, making it feasible for use on lightweight devices. The SPV contract enables the proof of transaction confirmations on the Bitcoin mainnet, which can then trigger predefined actions in the destination system. This paper also introduces innovative mechanisms for creating untraceable yet verifiable proofs of actions within the Bitcoin network, thereby enhancing privacy. By leveraging the SPV contract architecture, various use cases such as transaction status verification, cross-chain transactions, and decentralized applications can be efficiently implemented. Additionally, security considerations and optimizations like the use of zk-SNARKs for cost-effective Merkle proof verification are discussed. The paper concludes by exploring the potential of SPV contracts in facilitating trustless cross-chain interoperability and advancing the development of complex, automated financial instruments across blockchain ecosystems.

Machine learning-based ransomware classification of Bitcoin transactions

Ransomware presents a significant threat to the security and integrity of cryptocurrency transactions. This research paper explores the intricacies of ransomware detection in cryptocurrency transactions using the Bitcoinheist dataset. The dataset encompasses 28 distinct families classified into three ransomware categories: Princeton, Montreal, and Padua, along with a white category representing legitimate transactions. We propose a novel hybrid supervised and semi-supervised multistage machine learning framework to tackle this challenge. Our framework effectively classifies known ransomware families by leveraging ensemble learning techniques such as Decision Tree, Random Forest, XGBoost, and Stacking. Additionally, we introduce a novel semi-supervised approach to accurately identify previously unseen ransomware instances within the dataset. Through rigorous evaluation employing comprehensive classification metrics, including accuracy, precision, recall, F1 score, RoC score, and prediction time, our proposed approach demonstrates promising results in ransomware detection within cryptocurrency transactions.

Bitcoin Address Clustering Based on Change Address Improvement

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.

SHA-256 Hash Function on Intel DE10 Lite FPGA

Abstract: The SHA-256 hash function is a standardized and trusted algorithm that takes a set of data and produces a unique, deterministic, and irreversible representation called a hash or digest. A component of other protocols, SHA-256 protects password storage, secures and verifies Bitcoin transactions, and authenticates internet communication. We did a thorough analysis of the hash function and a preexisting Verilog implementation at the algorithmic, architectural, and circuit levels to identify and address the bottlenecks. We propose a new SHA-256 hardware architecture that utilizes binary tree structured adder trees to speed up hash computation. The proposed design targets Intel DE10-Lite FPGA and achieves 23% increase in computation speed. In applications, this can offer faster online communication or a more secure Bitcoin network.

Non-Intrusive Balance Tomography Using Reinforcement Learning in the Lightning Network

The Lightning Network (LN) is a second layer system for solving the scalability problem of Bitcoin transactions. In the current implementation of LN, channel capacity (i.e., the sum of individual balances held in the channel) is public information, while individual balances are kept secret for privacy concerns. Attackers may discover a particular balance of a channel by sending multiple fake payments through the channel. Such an attack, however, can hardly threaten the security of the LN system due to its high cost and noticeable intrusions. In this work, we present a novel non-intrusive balance tomography attack, which infers channel balances silently by performing legal transactions between two pre-created LN nodes. To minimize the cost of the attack, we propose an algorithm to compute the optimal payment amount for each transaction and design a path construction method using reinforcement learning to explore the most informative path to conduct the transactions. Finally, we propose two approaches (NIBT-RL and NIBT-RL- β ) to accurately and efficiently infer all individual balances using the results of these transactions. Experiments using simulated account balances over actual LN topology show that our method can accurately infer \(90\%\sim 94\% \) of all balances in LN with around 12 USD.

Robust recurrent graph convolutional network approach based sequential prediction of illicit transactions in cryptocurrencies

Money laundering has urged the need for machine learning algorithms for combating illicit services in the blockchain of cryptocurrencies due to its increasing complexity. Recent studies have revealed promising results using supervised learning methods in classifying illicit Bitcoin transactions of Elliptic data, one of the largest labelled data of Bitcoin transaction graphs. Nonetheless, all learning algorithms have failed to capture the dark market shutdown event that occurred in this data using its original features. This paper proposes a novel method named recurrent graph neural network model that extracts the temporal and graph topology of Bitcoin data to perform node classification as licit/illicit transactions. The proposed model performs sequential predictions that rely on recent labelled transactions designated by antecedent neighbouring features. Our main finding is that the proposed model against various models on Elliptic data has achieved state-of-the-art with accuracy and f1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f_1$$\\end{document}-score of 98.99% and 91.75%, respectively. Moreover, we visualise a snapshot of a Bitcoin transaction graph of Elliptic data to perform a case study using a backward reasoning process. The latter highlights the effectiveness of the proposed model from the explainability perspective. Sequential prediction leverages the dynamicity of the graph network in Elliptic data.

Bitcoin price and return prediction based on LSTM

This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.

Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost

With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network (Hybrid-CTEN), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.

Bitcoin in Conventional Markets: A Study on Blockchain-Induced Reliability, Investment Slopes, Financial and Accounting Aspects

Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.

Disorder unleashes panic in bitcoin dynamics

The behaviour of Bitcoin owners is reflected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reflected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose configurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to find that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.

A Study of Existing Approaches to Transaction Analysis in the Bitcoin Network

Today, the Bitcoin network faces a number of challenges, such as flawed user identification and fraudulent transaction methods that are used by criminals to conduct illegal activities. As a result, there is a growing need to improve existing tools for tracking transactions, as well as to develop new methods for identifying money in the Bitcoin network. The paper presents a study and systematization of the subject area problems, and also considers possible approaches to neutralize it. The purpose of this work is to analyze the existing methods of transaction verification of the Bitcoin network. Within the framework of the work, a system architecture is proposed, which includes a comprehensive approach to the process of transaction analysis. Each of the stages of this process is described: information gathering, aggregation, processing and analysis. An extended set of empirical rules (heuristics) for transaction analysis, which are used in existing clustering methods, is considered. The results of this work will provide an opportunity to improve the existing Bitcoin transaction verification methods and develop a new one with the possibility of increasing the efficiency of the process of identification of illegally obtained funds and their sources.

Bit Coin and the Challenges for Financial Regulation

Introduction: The main aim of this study is study is to understand the challenges that are faced by Bitcoin in financial regulation. Bitcoin helps to attract investors on a public ledger, which helps them to get back a significant return. Research objectives and research questions are highlighted in this section.
 Literature Review: Based on the research objectives, thematic analysis is highlighted. Therefore, Cryptocurrencies and Bitcoin in general have imposed numerous challenges in financial matters. The challenges Bitcoin faces due to its unique characteristics and decentralized phenomenon. Regulators are allowed to balance encouraging innovation in blockchain. Therefore, financial stability has to be maintained which helps to protect customers. After that, a clear tax guideline is required for the investors which help them to ensure compliance.
 Methodology: This study is based on the primary data collection method. Therefore, researchers used quantitative methods for collecting data. In this study, researchers can create a questionnaire set based on the 10 survey questions. In which 3 questions are based on demographic questions and 7 questions are based on variable-related questions. After that, researchers were able to collect information with the aid of 58 respondents.
 Findings: In this study, researchers are allowed to analyze the collected data with the aid of SPSS software. After that, based on the demographic test, researchers are able to identify the response rate of the participants. After that, “descriptive analysis”, and “hypothesis testing” play a significant role within this study 
 Discussion: Overall discussion about the research study is highlighted in this section. Therefore, challenges of Bitcoin transaction process is discussed within this section.
 Conclusion: This research study is based on the analysis of the challenges that are faced by investors during the transaction process. After that, basic knowledge about this study is gained by this study.

Cashing out crypto: state of practice in ransom payments

The fast pace of blockchain technology and cryptocurrencies’ evolution makes people vulnerable to financial fraud and provides a relatively straightforward monetisation mechanism for cybercriminals, in particular ransomware groups which exploit crypto’s pseudo-anonymity properties. At the same time, regulatory efforts for addressing crimes related to crypto assets are emerging worldwide. In this work, we shed light on the current state of practice of ransomware monetisation to provide evidence of their payment traceability, explore future trends, and—above all—showcase that over-regulating cryptocurrencies is not the best way to mitigate their risks. For that purpose, first, we provide an overview of the legislative initiatives currently taken by the USA, the EU, and the OECD to regulate cryptocurrencies, showing that strict laws and the divergences between the regulatory regimes can hardly efficiently regulate the global phenomenon of cryptocurrency, which transcends borders and states. Next, we focus on illicit payments in bitcoin to ransomware groups, illustrating how these payments are siphoned off and how criminals cash out the ransom, often leaving traceable evidence behind. To this end, we leverage a publicly available dataset and a set of state-of-the-art blockchain analysis tools to identify payment patterns, trends, and transaction trails, which are provided in an anonymised form. Our work reveals that a significant amount of illicit bitcoin transactions can be easily traced, and consequently, many cyber crimes like ransomware can actually be tracked down and investigated with existing tools and laws, thus providing fertile ground for better and fairer legislation on crypto.

Analyzing the peeling chain patterns on the Bitcoin blockchain

Bitcoin is a widely used decentralized cryptocurrency. The proportion of Bitcoin transactions used for illegal activities is increasing. Mixing services are commonly applied to enhance anonymity and make transaction records more challenging to follow and analyze. The current research on peeling chains is generally based on heuristic algorithms to identify change addresses. However, due to the characteristics and limitations of the Bitcoin blockchain, there is no such ground truth to ensure the accuracy of each derived change address. This research analyzes the peeling chain patterns based on self-change addresses. The use of self-change addresses implies that the input address and the address used for receiving the change are controlled by the same entity. Also, each chain's transaction details and generated chain parameters are further verified for more precise results. Combining the two methods ensures the accuracy of the extracted peeling chains to some extent. And the corresponding behavior pattern of the extracted chains is studied.

A Novel Covert Timing Channel Based on Bitcoin Messages

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional network-based covert channels. However, existing blockchain covert storage channels need to create a large number of transactions to transmit covert information. Creating transactions requires a transation fee, which means that the implementation of blockchain covert storage channels requires a high cost. Besides, created transactions remain on-chain permanently, leading to the threat of covert information being detected. To overcome these limitations, we propose a blockchain covert timing channel framework. Specifically, we utilize inv and getdata messages in the Bitcoin transaction broadcast as carriers and propose three modulation modes to achieve covert channels without cost and leaving no trace. We evaluate the concealment of our modes by K-S, KLD tests, and machine learning approaches. Experimental results show the indistinguishability between traffic carrying covert information and normal traffic. Our channels promise a capacity of 2.4 bit/s.

Bitcoin equilibrium dynamics: a long term approach

In the long run, Bitcoin transaction fees are the only source of revenue for miners. They compete broadly in two main ways: proof of work effort to win blocks; and transaction processing to gather fee rewards into the blocks they win. This paper contributes to existing literature by developing a dynamic model that separates these two functions, and explores implications for aggregate efficiency outcomes. Specifically, when set by free market forces (unrestricted by artificially imposed block size caps), what happens to overall transaction prices and quantities relative to total energy use? When is it worth Stackelberg-leading miners investing in efficiency-improving R&D? What effect does this have on overall efficiency over time? By explicitly separating specialised capital dedicated to SHA256 hashing (for proof of work) from transaction processing capital (for transaction collection and verification), this paper sheds light on these questions. One key conclusion is that miner innovation lowers energy use per transaction over time for elastic enough transaction demand schedules. The more competitors Bitcoin has (existing fiat and data services, and other new Blockchain-based systems), the stronger is this conclusion.

A Comprehensive Analysis of The Varied Interpretations among Islamic Scholars Concerning Bitcoin Transactions

A Comprehensive Analysis of The Varied Interpretations among Islamic Scholars Concerning Bitcoin Transactions

Identification of suspicious transactions in the bitcoin network: analysis, features, and machine learning algorithms

This article talks about one of the most popular cryptocurrencies today bitcoin. The principles of bitcoin and blockchain technologies are considered, where the proc and cons of bitcoin are listed. The focus of the research work is on the analysis of the use of cryptocurrencies, namely bitcoin for illegal and criminal purposes. Particular attention is paid to identifying a set of signs to identify suspicious activity in the bitcoin network. During research work reviewed and analyzed many scientific publications and articles, and from these studies, signs were identified that are key attributes when evaluating a Bitcoin transaction for suspicious or questionable transactions. Based on these features, an input dataset of 15 attributes with about 100,000 transactions was generated for further use in building a model that will identify suspicious bitcoin transactions. Using this dataset, models were created and trained to detect suspicious transactions in the bitcoin network based on several machine learning algorithms, such as random forest, logistic regression, k-nearest neighbors, decision tree. The article also provides comparisons of the results of machine learning algorithms, and in addition, selects the best algorithm that showed the best accuracy.