Bitcoin Network Research Articles

Approaching an Optimal Bitcoin Mining Overlay

Bitcoin builds upon an unstructured peer-to-peer overlay network to disseminate transactions and blocks. Broadcast in such a network is slow and brings inconsistencies, i.e. peers have different views of the system state. Due to the delayed block propagation and the competition of mining, forking, i.e. the blockchain temporarily diverges into two or more branches, occurs, which wastes computation power and causes security issues. This paper proposes an autonomous and distributed topology optimization mechanism to reduce block propagation delay and hence reduce the occurrence of blockchain forks. In the proposed mechanism, a node can autonomously update his neighbor set using the information provided by his current neighbors, since each neighbor will recommend a peer from his own neighbor set, i.e. a neighbor’s neighbor, to this node. Each recommendation is based on a peer’s propagation ability, which is characterized as a criteria function obtained through a combination of empirical analysis and machine learning. We further propose some metrics to evaluate a Bitcoin network topology. Experiment results reflect the effectiveness of the proposed mechanism and indicate the correlation between block propagation time and fork rate. Thus, we analyze the relation between block propagation time and fork rate by applying an epidemic model to capture the block propagation process. We prove that a Bitcoin network topology with a relatively small network delay variance among all nodes produces a lower fork rate than another topology if its average block propagation time to 84% of the entire network is shorter.

BMP: An Evaluation System for Bitcoin Mining Pools

With the in-depth research and cross-domain applications of blockchain in various industries, blockchain mining attracts great attention. A comprehensive and in-depth evaluation of mining pool is essential to the Bitcoin network supervision as well as the reduction of carbon emission. However, due to the difficulty of obtaining mining pool internal data, the current evaluation is limited to a single mining power indicator. In this article, we build a four-layer mining pool evaluation model, named BMP, based on the analytic hierarchy process (AHP). The model contains three first-level indicators and nine second-level indicators, covering various aspects of the performance of mining pool. Miner address extraction provides basic data for the calculation of these indicators. By utilizing the model proposed, we evaluated and ranked eight mainstream mining pools, which broadcasted 71.69% of the blocks during the period January 1, 2021-July 31, 2021. Through comparative analysis of evaluation result, we find that the 65% decline of mining power in June did not pose a threat to the stability of Bitcoin system. In addition, we also revealed the reasons for various fluctuations.

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.

EFFECTIVE FRAUD DETECTION IN BLOCKCHAIN USING XGBOOST WITH RANDOM FOREST

To solve the issue of fraud and abnormalities in the Bitcoin network, we offer a model. Here, we classify transactions based on integrated and fraudulent transaction patterns using machine learning methods like XGboost and Random Forest (RF). Then, future incoming transactions are predicted using the trained dataset. To identify fraudulent transactions, the authors use blockchain technology with machine learning algorithms. The proposed model performs a security analysis of the proposed smart contract to show the system's robustness and determines the precision and AUC of the models to gauge accuracy. To guard against assaults and vulnerabilities on the suggested system, the authors additionally suggest an attacker model. Overall, as financial technology advances, the proposed approach seeks to offer a more secure method for spotting fraud in the Bitcoin network. The authors assert that their suggested system's use of machine learning and blockchain technology may successfully identify and stop fraudulent transactions.

SofitMix: A Secure Offchain-Supported Bitcoin-Compatible Mixing Protocol

Privacy preservation is highly expected in the Bitcoin Network. However, only applying pseudonyms cannot completely ensure anonymity/unlinkability between payers and payees. Current approaches mainly depend on a mixer service, which obfuscates payer-payee relationships of transactions. While the mixer service improves transaction privacy, it still suffers from some severe security threats (e.g., DoS attack and collusion attack), and does not support effective and reliable off-chain payment in a parallel mode. In this paper, we propose a mixing protocol for the Bitcoin Network based on zero-knowledge proof, called SofitMix. It is the first mixing protocol that can effectively resist both the DoS attack and the collusion attack. It can also support a set of parallel off-chain payments in a reliable way no matter whether some payers abort a transaction. We analyze and prove SofitMix security following the Universal Composability model with regard to fair exchange, unlinkability, collusion-resistance, DoS-resistance and Sybil-resistance. Through a proof-of-concept implementation, we demonstrate its validity and fairness. We also show its advance on off-chain payment reliability and DoS attack resistance, compared to TumbleBit.

Analyzing Bitcoin's Decentralization: Coefficient of Variation Approach and 21 Million Divisibility

Bitcoin, a decentralized digital currency, has received a lot of interest due to its potential to challenge the traditional financial system. This study applies a coefficient of variation analysis with an emphasis on the idea of dividing the entire supply by 21 million to determine the degree of decentralization for Bitcoin. The coefficient of variation is a statistical indicator of diffusion, making it possible to analyze how many Bitcoins different stakeholders hold. We can learn more about the degree of wealth concentration or distribution among Bitcoin users by examining the degree of decentralization using this method. This research calculates the coefficient of variation using publicly available information on Bitcoin addresses and the holdings linked to those addresses. The findings will contribute to the ongoing discourse on Bitcoin's decentralization and shed light on the potential impact of the fixed supply of 21 million Bitcoins on wealth distribution within the Bitcoin network.

System-Level Dependability Analysis of Bitcoin under Eclipse and 51% Attacks

Bitcoin is an electronic cryptocurrency developed based on Blockchain technology. With its decentralized feature, it has become incredibly popular since its invention. However, the Bitcoin network suffers from 51% attacks, where if malicious attackers’ control over half of the computing power, they are able to rewrite the network. The attackers are capable of doing so by initiating the Eclipse attack first, which aims to monopolize all communications from and to a controlled Bitcoin node. In this paper, we model and analyze the dependability of the Bitcoin network subject to the Eclipse and 51% attacks. We propose a hierarchical model that encompasses a continuous-time Markov chain method for the node-level dependability analysis and a multi-valued decision diagram method for the system-level dependability analysis. Detailed case studies on Bitcoin systems with homogeneous and heterogeneous nodes are conducted to demonstrate the proposed model and investigate the impacts of several critical parameters on Bitcoin network dependability.

Bitcoin under the microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories – miners, exchanges, services, retail wallets and receiving-only addresses – and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users.

Bitcoin network-based anonymity and privacy model for metaverse implementation in Industry 5.0 using linear Diophantine fuzzy sets

AbstractMetaverse is a new technology expected to generate economic growth in Industry 5.0. Numerous studies have shown that current bitcoin networks offer remarkable prospects for future developments involving metaverse with anonymity and privacy. Hence, modelling effective Industry 5.0 platforms for the bitcoin network is crucial for the future metaverse environment. This modelling process can be classified as multiple-attribute decision-making given three issues: the existence of multiple anonymity and privacy attributes, the uncertainty related to the relative importance of these attributes and the variability of data. The present study endeavours to combine the fuzzy weighted with zero inconsistency method and Diophantine linear fuzzy sets with multiobjective optimisation based on ratio analysis plus the multiplicative form (MULTIMOORA) to determine the ideal approach for metaverse implementation in Industry 5.0. The decision matrix for the study is built by intersecting 22 bitcoin networks to support Industry 5.0's metaverse environment with 24 anonymity and privacy evaluation attributes. The proposed method is further developed to ascertain the importance level of the anonymity and privacy evaluation attributes. These data are used in MULTIMOORA. A sensitivity analysis, correlation coefficient test and comparative analysis are performed to assess the robustness of the proposed method.

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.

Rethinking selfish mining under pooled mining

Bitcoin’s core security requires honest participants to control at least 51% of the total hash power. However, it has been shown that several techniques can exploit the fair mining in the Bitcoin network. This study focuses on selfish mining, which is based on the idea, ”keeping blocks secret.” Herein, we analyze selfish mining regarding competition between mining pools. We emphasize that mining-related information is shared between mining pools and participants. Based on shared information about selfish mining, we have developed an effective and practical counter strategy.

A secure payment channel rebalancing model for layer-2 blockchain

The concept of cryptocurrencies is prevalently used in the financial world. Some of the currencies like Bitcoin and Ethereum have shown promising outputs. The scalability problem of these cryptocurrencies is an ongoing challenge, solved through many facets. Bitcoin Lightning Network, a Layer-2 solution to Bitcoin Network, is an off-chain crypto transaction platform working on payment channels to transact bi-directionally between two parties. When the maximum payments flow is unidirectional, the individual balances of the parties in the direction of payment get exhausted. It results in the transaction failing for future payments in that direction and lowers the feasibility of network usage. In this paper, we propose a heuristic-based distributed solution by preserving the privacy and anonymity of the parties involved in the rebalancing procedure and improving the fund transfer capability of the channels. The rebalancing mechanism has a global solution that can rebalance to the maximum extent and a local solution using a significantly lower fee. Again the indirect and direct rebalancing schemes balance the imbalanced channel and prevent further transaction failure and closure of channels due to insufficient balance. The resultant method also has an updation in the HTLC message exchange protocol to avoid the Flood and Loot attack that may arise to maintain the safe state of every honest participant in the network.

Bitcoin mempool growth and trading volumes: Integrated approach based on QROF Multi-SWARA and aggregation operators

Investors are looking for objects in which they invest their funds successfully, evaluating the effectiveness of alternative markets and their instruments. Historically, cash flow indicators most effectively reflected the mood of the masses in relation to any financial asset, both in the short-and long-term. This article examines in detail the queue of already completed, but not confirmed transactions in the bitcoin network. The mempool is able to timely display the growth in the number of transactions awaiting confirmation, which makes it a leading indicator of future cash flows that could affect the trading volumes and market prices of bitcoin. This study evaluates bitcoin mempool priorities and two different analyses have been conducted for this purpose. Firstly, the mempool periods are examined through a statistical analysis. Secondly, the performance determinants of mempool are assessed with q-ROF Multi-SWARA. In addition to q-ROF sets, weights are computed with IFS and PFS. Demonstrated here is that the results of all fuzzy sets are identical. This outcome explains the reliability of the findings and they indicate that a transaction is the most important determinant of the bitcoin mempool. It emerged that the adjusted mempool data (+16.7%) for 7-day and 30-day moving averages was able, with a time lag of 24–48 h, to indicate significant volatility of future bitcoin trading volumes (+1.6%) on average. The obtained values confirm the empirical conclusion reached here that the mempool growth leads to cash flow growth. An increase in future cash flows results in a substantial rise in future trading volumes. The key takeaway from the analysis is that mempool is able to effectively predict future increases in trading volumes based on the prior cash flow growth projected into mempool growth. However, as a price indicator, mempool does show mixed results with mostly uncertainty in the direction of price movement.

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.

Characterizing the Bitcoin network topology with Node‐Probe

AbstractIn blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node‐Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node‐Probe, we have collected 5‐week‐long snapshots (36‐day‐long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long‐running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity compared with the Bitcoin default protocol. Considering a K‐anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.

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.

Conditions for advantageous quantum Bitcoin mining

Our aim is to determine the conditions for quantum computing technology to give rise to the security risks associated with quantum Bitcoin mining. Specifically, we determine the speed and energy efficiency a quantum computer needs to offer an advantage over classical mining. We analyze the setting in which the Bitcoin network is entirely classical except for a single quantum miner with a small hash rate compared to the network. We develop a closed-form approximation for the probability that the quantum miner successfully mines a block, with this probability dependent on the number of Grover iterations the quantum miner applies before making a measurement. Next, we show that for a quantum miner that is “peaceful”, this success probability is maximized if the quantum miner applies Grover iterations for 16 ​min before measuring, which is surprising, as the network mines blocks every 10 ​min on average. Using this optimal mining procedure, we show that the quantum miner outperforms a classical computer in efficiency (cost per block) if the condition Q ​< ​Crb is satisfied, where Q is the cost of a Grover iteration, C is the cost of a classical hash, r is the quantum miner's speed in Grover iterations per second, and b is a factor that attains its maximum if the quantum miner uses our optimal mining procedure. This condition lays the foundation for determining when quantum mining and the known security risks associated with it will arise.

Comparison Study of the Top 5 Leading Cryptocurrencies based on General Consensus Protocol: Bitcoin, Ethereum, Tether, XRP and Bitcoin Cash

Bitcoin has had a tremendous impact on the monetary system around the globe today since its launch in 2009 by its founder, Satoshi Nakamoto. Since then, over three thousand cryptocurrencies have risen to compete with traditional fiat currencies. While many experts believe that cryptocurrencies are more comparable to assets like gold, few others believe that cryptocurrencies could replace traditional fiat currencies as a medium of payment, just as Satoshi envisioned when he published the Bitcoin protocol. As such, there exist few papers in the literature discussing the potential for Bitcoin to become a major payment currency. Nevertheless, there is a lack of research in evaluating whether Bitcoin is set to dominate the market as a payment currency, or whether another cryptocurrency would take the lead. In addition, the issue of strict regulation capping the potential of cryptocurrencies has been well-studied in the literature and, hence, this paper tries to evaluate the top five leading cryptocurrencies (based on their market cap value as of July 2020) from a different perspective. Particularly, the evaluation is based on five benchmark evaluation factors: Speed, Activity, Decentralization, Users, and Community. For this preliminary study, we assign all these benchmarks an equal weight. Our conclusion shows that Ethereum could be more suitable as a future payment currency rather than Bitcoin.

Cryptocurrency: A New Millennium Currency (Problem and Prospects in India)

Abstract: Crypto currency is the latest and the most significant development in the financial industry. It has brought a revolution in the Indian financial markets and created a challenge for the banking industry by presenting it as investment opportunity. Bit coin, the first and the most popular crypto currency launched in the year 2009. It has given the other option to the long standing and unchanged financial payment system which is existing for many decades. It is a decentralized digital currency that can be sent from user to user on the peer-to-peer. In the bitcoin network it can be sent without the need for intermediaries, where transactions are done through a public ledger called block chain, where user data is stored anonymously. It has created a robust digital trade markets where there is free flowing trading system without fees. They are not going to replace the traditional system of payments but they have changed the way of making payments through internet and interaction of global markets. In addition to this has given an investment option too. In the light of above facts current research has been conducted to know the status of cryptocurrency in India, various types of crypto currencies existing in India its problems and prospects. It was found that there is no legal regulation to control their operation only current announcement in the budget gains arising from the trading of these currencies has brought under taxation. But till date RBI has not given any recognition to it and the user /investor is responsible for their risk.

An Optimised Bitcoin Mining Strategy

Stale blocks are not avoidable in blockchain, such as the Bitcoin network, when proof-of-work is used as the consensus protocol. However, as the economic loss to the miners and the security risk to the network cannot be ignored, research is needed to identify and analyse stale blocks. By analysing the factors influencing the generation of stale blocks, the authors propose a new machine learning model based on XGBoost. They propose a new data collection method for bitcoin nodes to obtain real data for training prediction model. Then, based on the model, they generate optimal mining strategies and analyse the economic benefits. The experimental data and application cases show that the real-time data detection and machine learning model that they propose can accurately identify and predict the generation of stale blocks and generate an economically optimal mining strategy in the Bitcoin network with the presence of stale blocks.