Bitcoin Addresses Research Articles

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

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.

Automating Collision Attacks on RIPEMD-160

As an ISO/IEC standard, the hash function RIPEMD-160 has been used to generate the Bitcoin address with SHA-256. However, due to the complex doublebranch structure of RIPEMD-160, the best collision attack only reaches 36 out of 80 steps of RIPEMD-160, and the best semi-free-start (SFS) collision attack only reaches 40 steps. To improve the 36-step collision attack proposed at EUROCRYPT 2023, we explored the possibility of using different message differences to increase the number of attacked steps, and we finally identified one choice allowing a 40-step collision attack. To find the corresponding 40-step differential characteristic, we re-implement the MILP-based method to search for signed differential characteristics with SAT/SMT. As a result, we can find a colliding message pair for 40-step RIPEMD-160 in practical time, which significantly improves the best collision attack on RIPEMD-160. For the best SFS collision attack published at ToSC 2019, we observe that the bottleneck is the probability of the right-branch differential characteristics as they are fully uncontrolled in the message modification. To address this issue, we utilize our SAT/SMT-based tool to search for high-probability differential characteristics for the right branch. Consequently, we can mount successful SFS collision attacks on 41, 42 and 43 steps of RIPEMD-160, thus significantly improving the SFS collision attacks. In addition, we also searched for a 44-step differential characteristic, but the differential probability is too low to allow a meaningful SFS collision attack.

Machine learning in classifying bitcoin addresses

The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.

A Proposal for an Open-Source Bitcoin Forensics Tool

Over recent decades, the global financial paradigm has experienced significant transformations, notably the emergence and adoption of cryptocurrencies. The escalating prominence of assets like Bitcoin has inadvertently catalysed a surge in illicit activities associated with the currency. Consequently, the forensic examination of transactions within blockchains becomes imperative for the detection and surveillance of malevolent undertakings. This research delineates a preliminary pipeline for a Bitcoin forensic analysis tool. Moving forward, the ambition is to conceptualize and empirically validate this tool utilizing data procured from blockchain and ancillary sources. The methodology will harness Open-Source Intelligence (OSINT), clustering of Bitcoin addresses, and an exhaustive financial analysis. Upon finalizing the pipeline, the implementation of an open-source instrument is envisioned, poised to confer substantial advantages to the broader cryptocurrency milieu.

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.

Digital Forensic Readiness Model for Internet Voting

Voting is an exercise of choosing a preferred candidate through a process called an election. In many countries, this exercise is a basic human right. In every election process, there are some pre-requisite processes and procedures which must be set up first. These are essential in the pre-vote-casting stage, during vote-casting and post-vote-casting stage. Electoral disagreements amongst stakeholders and parties of interest are usually experienced in each of the above-mentioned voting process stages. The main points of conflict in an election process are vote rigging and vote fraud. Failure to amicably mitigate these issues can result in a criticised/rejected election result. Therefore, this research aims to address the problem of vote rigging and vote fraud allegations in an election process. The resolution thereof is achieved through the introduction of an online based voting system which is supported by a digital forensic readiness mechanism. Online voting system gives citizens the flexibility to use internet-enabled devices such as cell phones and laptops to cast their votes in a safe, secrete and secure protocol. To address the problem of vote rigging and vote fraud, the online voting system is integrated with cyber security and vote protection mechanisms. The cyber security and vote protection mechanism is based on Blockchain algorithms. A Blockchain-based voting process is a peer-to-peer mechanism where a decentralised database is used to store data. Tokens move directly from one peer (voter) to another peer (candidate). The results are tallied by counting the number of tokens paid to each candidate. Each voter is allocated a Bitcoin token and each candidate is allocated a Bitcoin address. During vote casting, the voter transfers their Bitcoin token into the wallet of a registered candidate. At the end of the voting process, the total number of Bitcoin tokens transferred to each candidate is counted and tallied up. The wallet is loaded with only one Bitcoin token, hence there is no possibility of double voting. The model ensures vote security, anonymity, auditability, accountability, accuracy and uniqueness.

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> .

Application of Big Data Mining Technology in Blockchain Computing

Big data in the modern science and technology and social activities play an important role, on the one hand, a large number of new applications and technology into our lives, in the use of these new technologies to produce a large amount of data, on the other hand, big data as one of the most important digital assets, many of the development of new technologies also rely on large data as support. This paper focuses on the research and application of big data mining technology in blockchain computing. Firstly, this paper extracts the corresponding transaction data according to the Bitcoin address and constructs the transaction features to get the Bitcoin data set. Then, the data features are processed. Then, three algorithm models, SVM, Adaboost and Random Forest, are selected to model and analyze the preprocessed data combined with different sampling strategies. According to the comprehensive performance of the model and its shortcomings, the model is selected and improved.

Functional classification of bitcoin addresses

A classification model for predicting the main activity of bitcoin addresses based on their balances is proposed. Since the balances are functions of time, methods from functional data analysis are applied; 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. The proposed 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.

Blockchain analysis of the Bitcoin market. (Part 3)

The detailed analysis of the Bitcoin network and its main participants. The expert authors (Igor Makarov, London School of Economics, Antoinette Schoar, MIT Sloan School of Management) completed the study authorized by the National Bureau of Economic Research (NBER), the US-based private agency. The Bitcoin network is defined as a new database comprising many of public and proprietary sources to link bitcoin address to real object, and an extensive set of algorithms to extract information on market key players behavior. Three major pieces of analysis of the Bitcoin eco-system were conducted. First, the authors analyze the transaction volume and network structure of the main participants on the blockchain. Second, they document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, they analyze the ownership concentration of the largest holders of Bitcoin. The researchers found that 1/3 of all bitcoins issued were owned by 10,000 individual investors. They conclude that the high concentration makes the first cryptocurrency market vulnerable to hypothetical hacker attack. The translator notes that paraphrasing English text in Russian was rather challenging due to the newness of the financial agenda and introduction of the term entity extensively used in the Western countries though new to Russia. Nevertheless, it is necessary to introduce readers to the bitcoin technology which will be also practical and useful for the library and information community.

Improving Address Clustering in Bitcoin by Proposing Heuristics

The Bitcoin system uses anonymous transactions to protect users&#x2019; privacy, but attackers can use this defect of bitcoin transactions to discover the association between bitcoin addresses. At present, address clustering methods can make use of these vulnerabilities to associate the address as an entity to a certain extent. However, these address clustering methods have problems such as an insufficient inference rate of change addresses, inability to identify mixing transactions, and low efficiency of algorithm implementation. We propose some solutions to these problems. 1) We improve the method of change address identification to identify and mark more of them. 2) We propose a heuristic address clustering method related to mixing transactions, which can identify their privacy vulnerabilities. 3) We propose an incremental address clustering method that can store the historical state and more quickly discover the anonymity defect of Bitcoin. We use real Bitcoin transaction data to demonstrate our method&#x2019;s feasibility and reliability.

Blockchain analysis of the Bitcoin market. (Part 2)

The detailed analysis of the Bitcoin network and its main participants. The expert authors (Igor Makarov, London School of Economics, Antoinette Schoar, MIT Sloan School of Management) completed the study authorized by the National Bureau of Economic Research (NBER), the US-based private agency. The Bitcoin network is defined as a new database comprising many of public and proprietary sources to link bitcoin address to real object, and an extensive set of algorithms to extract information on market key players behavior. Three major pieces of analysis of the Bitcoin eco-system were conducted. First, the authors analyze the transaction volume and network structure of the main participants on the blockchain. Second, they document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, they analyze the ownership concentration of the largest holders of Bitcoin. The researchers found that 1/3 of all bitcoins issued were owned by 10,000 individual investors. They conclude that the high concentration makes the first cryptocurrency market vulnerable to hypothetical hacker attack. The translator notes that paraphrasing English text in Russian was rather challenging due to the newness of the financial agenda and introduction of the term entity extensively used in the Western countries though new to Russia. Nevertheless, it is necessary to introduce readers to the bitcoin technology which will be also practical and useful for the library and information community.

Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis

Bitcoin is a cryptocurrency based on blockchain. All historical Bitcoin transactions are stored in the Bitcoin blockchain, but Bitcoin owners are generally unknown. This is the reason for Bitcoin's pseudo-anonymity, therefore it is often used for illegal transactions. Bitcoin addresses are related to Bitcoin users' identities. Some Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin transactions. However, existing Bitcoin analysis methods do not consider the fusion of new blocks' data, resulting in low efficiency of Bitcoin address analysis. In order to address this problem, this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is added. Besides, a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin Blockchain. Experimental results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.

Special Subsets of Addresses for Blockchains Using the secp256k1 Curve

In 2020, Sala, Sogiorno and Taufer were able to find the private keys of some Bitcoin addresses, thus being able to spend the cryptocurrency linked to them. This was unexpected since the recovery of non-trivial private keys for blockchain addresses is deemed to be an infeasible problem. In this paper, we widen this analysis by mounting a similar attack on other small subsets of the set of private keys. We then apply it to other blockchains as well, examining Ethereum, Dogecoin, Litecoin, Dash, Zcash and Bitcoin Cash. In addition to the results, we also explain the techniques we have used to perform this exhaustive search for all the addresses that have ever appeared in these blockchains, and we give an estimate of the time needed to perform all the computations. Finally, we also examine the possibility of mounting a similar attack on other elliptic curves used in blockchains, i.e., Curve25519 and NIST P-256.

CRYPTOCURRENCY MARKET IN UKRAINE: CURRENT STATE AND DEVELOPMENT PROSPECTS IN THE CONTEXT OF INTERNATIONAL MONETARY AND CREDIT RELATIONS

Purpose. The aim of the article to work out the theoretical and practical aspects of the functioning and development prospects of the cryptocurrency market in Ukraine in the context of international monetary and credit relations.. Methodology of research. The theoretical and methodological basis of the research is the fundamental provisions of modern economic theory, scientific works of scientists. General scientific and special methods were used in the research process, in particular: dialectical, monographic and systemic analysis – to substantiate the essence of the cryptocurrency market; analysis and synthesis – for studying the object and subject of research; comparative analysis – to compare economic indicators in time and space; graphic – with a visual representation of the dynamics and structural changes of the cryptocurrency market; constructive and experimental – to form a decision with the help of analytical support; abstract and logical – during the formation of theoretical generalizations, assumptions, conclusions. Findings. The peculiarities of the development of the cryptocurrency market in Ukraine as a component of the financial market are considered. Ukraine's place in the Global Cryptocurrency Adoption Index has been studied and analysed. Originality. It has been proven that there is correlation dependence between the prices of Bitcoin and Ether cryptocurrency coins on the Ukrainian cryptocurrency market, and there is no multicollinearity between Ether and Litecoin coins. It was found that the correlation between cryptocurrency mining and their price is average. Prospective directions for the development of the cryptocurrency market in Ukraine are offered. Practical value. The main results of the conducted research will create favourable conditions for a better understanding of cryptocurrency as a new type of money, which will make it possible to use it more widely in the economic activities of economic entities and countries. Key words: financial market, cryptocurrency market, cryptocurrency, crypto coin, cryptocurrency coin, Bitcoin address, Bitcoin, Ether, Litecoin.

Front cover: Bitcoin address clustering method based on multiple heuristic conditions

Front cover: Bitcoin address clustering method based on multiple heuristic conditions

Bitcoin address clustering method based on multiple heuristic conditions

AbstractSingle heuristic method and incomplete heuristic conditions were difficult to cluster a large number of addresses comprehensively and accurately. Therefore, this paper analysed the associations between Bitcoin transactions and addresses and used six heuristic conditions to cluster addresses and entities. We proposed an improved change address detection algorithm and compared it with the original change address algorithm to prove the effectiveness of the improved algorithm. By adding conditional constraints, the identified change address was more accurate, and the convergence speed of the algorithm was accelerated. Our work presented the pseudo‐anonymity mechanism of the Bitcoin system, which could be used by the law enforcement agencies to track and crack down illegal transactions.

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.

BEvote: Bitcoin-Enabled E-Voting Scheme with Anonymity and Robustness

When building the large-scale distributed decision control system based on mobile terminal devices (MTDs), electronic voting (E-voting) is a necessary technique to settle the dispute among parties. Due to the inherent insecurity of Internet, it is difficult for E-voting to attain complete fairness and robustness. In this study, we argue that Bitcoin blockchain offers better options for a more practical E-voting. We first present a coin mixing-based E-voting system model, which can cut off the relationship between the voter’s real identity and its Bitcoin address to achieve strong anonymity. Moreover, we devise a secret sharing-based E-voting protocol, which can prevent voting number from being leaked ahead and further realize strong robustness. We establish the probable security theory to prove its security. In addition, we use the experimental evaluation to demonstrate its efficiency.