Blockchain-Research and Applications最新文献_第8页

Understanding the market potential of crypto mining with quantum mechanics and golden cut-based picture fuzzy rough sets 利用量子力学和基于黄金分割的图像模糊粗糙集了解加密货币挖矿的市场潜力

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100230

Hasan Dincer , Serhat Yüksel , Gabor Pinter , Alexey Mikhaylov

Significant improvements should be made to increase the market potential of crypto mining. However, it is not financially feasible to make too many improvements because all actions lead to cost increases. In this context, it is necessary to determine the factors that most affect this process. Accordingly, the purpose of this study is to understand the main indicators that can improve the market potential of crypto mining activities. Therefore, the main research question of this study is to identify which factors should be prioritized while generating appropriate strategies to increase these activities. In this context, a new model has been constructed to answer this question. First, significant indicators are identified based on the evaluation of the literature. After that, these factors are weighted via quantum picture fuzzy rough set-based M-SWARA. The main contribution of this study is the generation of a new decision-making model to understand the key issues related to the market potential of crypto mining activities. The M-SWARA model is taken into consideration for criteria weighting. Owing to this issue, the causal relationships between the items can be identified. The findings demonstrate that reducing energy costs emerges as the most important factor for improving the market potential of the crypto mining industry. Furthermore, technological developments also play an important role in this regard.

应该做出重大改进，以增加加密挖矿的市场潜力。然而，做太多的改进在财务上是不可行的，因为所有的行动都会导致成本的增加。在这种情况下，有必要确定最影响这一进程的因素。因此，本研究的目的是了解可以提高加密货币挖矿活动市场潜力的主要指标。因此，本研究的主要研究问题是确定哪些因素应该优先考虑，同时产生适当的策略来增加这些活动。在这种背景下，一个新的模型已经被构建来回答这个问题。首先，在文献评价的基础上，确定重要指标。然后，通过基于量子图像模糊粗糙集的M-SWARA对这些因素进行加权。本研究的主要贡献是生成了一个新的决策模型，以理解与加密采矿活动的市场潜力相关的关键问题。采用M-SWARA模型对标准进行加权。由于这个问题，可以确定项目之间的因果关系。研究结果表明，降低能源成本是提高加密货币挖矿行业市场潜力的最重要因素。此外，技术发展在这方面也起着重要作用。

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引用次数: 0

Integrating blockchain technology within an information ecosystem 在信息生态系统中集成区块链技术

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100225

Francesco Salzano , Lodovica Marchesi , Remo Pareschi , Roberto Tonelli

Context: Blockchain-based information ecosystems (BBIEs) are a type of information ecosystem in which blockchain technology is used to provide a trust mechanism among parties and to manage shared business logic, breaking the traditional scheme of information ecosystems dominated by a leading company and leveraging the decentralization of data management, information flow, and business logic.

Objective: In this paper, we'd like to propose an architecture and the technical aspects concerning creating a BBIE, underlining the supplied advantages and the logic decomposition among the business and storage components.

Method: The requirements are derived from the current needs of the collaborative business and the data collected by surveying practitioners. To meet these needs, we followed the Grounded Theory research approach. We validate our architectural schema against a case study on managing a wine supply chain involving different companies and supervision authorities.

Results: The proposed solution integrates blockchain-based applications with the existing information system as a module of the ecosystem, leveraging on the low costs, scalability, and high-level security because of the restricted access to the network.

Conclusion: We must go a long way in deepening and refining the possibilities offered by technology in supporting innovative multi-organizational business models. BBIEs can contribute substantially to paving the way in such a direction.

背景：基于区块链的信息生态系统（BBIEs）是一种信息生态系统，利用区块链技术提供各方之间的信任机制，管理共享的业务逻辑，打破传统的由一家龙头公司主导的信息生态系统方案，利用数据管理、信息流和业务逻辑的去中心化。目的：在本文中，我们将提出创建BBIE的体系结构和技术方面，强调所提供的优势以及业务和存储组件之间的逻辑分解。方法：需求来源于协作业务的当前需求和测量从业人员收集的数据。为了满足这些需求，我们采用了扎根理论的研究方法。我们根据一个涉及不同公司和监管机构的葡萄酒供应链管理案例研究来验证我们的架构模式。结果：提出的解决方案将基于区块链的应用程序与现有信息系统作为生态系统的一个模块集成在一起，利用低成本、可扩展性和高安全性，因为网络访问受限。结论：在深化和完善技术为支持创新的多组织商业模式提供的可能性方面，我们必须走很长的路。bie可以为朝着这个方向铺平道路作出重大贡献。

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引用次数: 0

Robust cooperative spectrum sensing in cognitive radio blockchain network using SHA-3 algorithm 基于SHA-3算法的认知无线电区块链网络鲁棒协同频谱感知

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100224

Evelyn Ezhilarasi I, J. Christopher Clement

Cognitive radio network (CRN) uses the available spectrum resources wisely. Spectrum sensing is the central element of a CRN. However, spectrum sensing is susceptible to multiple security breaches caused by malicious users (MUs). These attackers attempt to change the sensed result in order to decrease network performance. In our proposed approach, with the help of blockchain-based technology, the fusion center is able to detect and prevent such criminal activities. The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing. The detection strategy takes place in two stages: block updation phase and iron out phase. The simulation results of the proposed method demonstrate 3.125%, 6.5%, and 8.8% more detection probability at −5 dB signal-to-noise ratio (SNR) in the presence of MUs, when compared to other methods like equal gain combining (EGC), blockchain-based cooperative spectrum sensing (BCSS), and fault-tolerant cooperative spectrum sensing (FTCSS), respectively. Thus, the security of cognitive radio blockchain network is proved to be significantly improved.

认知无线电网络（Cognitive radio network， CRN）明智地利用可用的频谱资源。频谱感知是CRN的核心要素。然而，频谱感知容易受到恶意用户（MUs）的多重安全漏洞的影响。这些攻击者试图改变感知结果，以降低网络性能。在我们提出的方法中，借助基于区块链的技术，融合中心能够检测和预防此类犯罪活动。我们的模型方法利用基于区块链的MU检测与SHA-3哈希和基于能量检测的频谱感知。检测策略分为两个阶段：块更新阶段和清除阶段。仿真结果表明，与等增益组合（EGC）、基于区块链的合作频谱感知（BCSS）和容错合作频谱感知（FTCSS）等方法相比，该方法在−5 dB信噪比（SNR）下的检测概率分别提高了3.125%、6.5%和8.8%。从而证明认知无线电区块链网络的安全性得到了显著提高。

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引用次数: 0

Looking for stability in proof-of-stake based consensus mechanisms 寻找基于股权证明的共识机制的稳定性

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100222

Alberto Leporati, Lorenzo Rovida

The Proof-of-Stake (PoS) consensus algorithm has been criticized in the literature and in several cryptocurrency communities, due to the so-called compounding effect: who is richer has more coins to stake, therefore a higher probability of being selected as a block validator and obtaining the corresponding rewards, thus becoming even richer. In this paper, we present a PoS simulator written in the Julia language that allows one to test several variants of PoS-based consensus algorithms, tweak their parameters, and observe how the distribution of cryptocurrency coins among users evolves over time. Such a tool can be used to investigate which combinations of parameter values allow to obtain a “fair” and stable consensus algorithm, in which, over the long term, no one gets richer or poorer by the mere act of validating blocks. Based on this investigation, we also introduce a new PoS-based consensus mechanism that allows the system to keep the wealth distribution stable even after a large number of epochs.

由于所谓的复合效应，权益证明（PoS）共识算法在文献和几个加密货币社区中受到批评：更富有的人拥有更多的硬币，因此更有可能被选为区块验证者并获得相应的奖励，从而变得更加富有。在本文中，我们提出了一个用Julia语言编写的PoS模拟器，该模拟器允许测试基于PoS的共识算法的几种变体，调整其参数，并观察加密货币在用户之间的分布如何随着时间的推移而演变。这样的工具可以用来研究参数值的哪些组合允许获得“公平”和稳定的共识算法，在这种算法中，从长远来看，没有人仅仅通过验证块的行为而变得更富有或更贫穷。在此基础上，我们还引入了一种新的基于pos的共识机制，使系统即使经过大量的时代也能保持财富分配的稳定。

引用次数: 0

A prototype model of zero trust architecture blockchain with EigenTrust-based practical Byzantine fault tolerance protocol to manage decentralized clinical trials 基于特征信任的实用拜占庭容错协议的零信任架构区块链原型模型，用于分散临床试验的管理

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100232

Ashok Kumar Peepliwal , Hari Mohan Pandey , Surya Prakash , Sudhinder Singh Chowhan , Vinesh Kumar , Rahul Sharma , Anand A. Mahajan

The COVID-19 pandemic necessitated the emergence of Decentralized Clinical Trials (DCTs) due to patient retention, accelerating trials, improving data accessibility, enabling virtual care, and facilitating seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the IoT has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done for immutability, privacy and security, mutual consensus, transparency, accountability, tracking and tracing, and temperature‒humidity control parameters.

COVID-19大流行使分散临床试验（dct）的出现成为必要，因为患者保留、加速试验、改善数据可访问性、实现虚拟护理以及通过综合系统促进无缝通信。然而，在dct中集成系统会使临床数据暴露于潜在的安全威胁中，使它们在任何阶段都容易被窃取，存在协议偏差和监测问题的高风险。为了缓解这些挑战，区块链技术作为一个安全框架，作为一个分散的分类账，通过建立一个零信任架构来创建一个不可变的环境，在这个架构中，数据在经过验证之前被认为是不可信的。与支持物联网（IoT）的可穿戴设备相结合，区块链在DCT自动化和操作期间确保临床试验数据在私有区块链上的传输。本文提出了零信任架构区块链（z-TAB）的原型模型，用于在DCT手术管理中整合患者生成的临床试验数据。基于特征信任的实用拜占庭容错（T-PBFT）算法已被纳入共识协议，利用超级账本结构。此外，物联网已被集成，以简化区块链平台内利益相关者之间的数据处理。对不变性、隐私和安全性、相互共识、透明度、问责制、跟踪和跟踪以及温湿度控制参数进行了严格的评估。

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引用次数: 0

A deep decentralized privacy-preservation framework for online social networks 一个深度分散的在线社交网络隐私保护框架

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100233

Samuel Akwasi Frimpong , Mu Han , Emmanuel Kwame Effah , Joseph Kwame Adjei , Isaac Hanson , Percy Brown

This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. We propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities. Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. Additionally, a blockchain network secures users’ data via smart contracts, ensuring robust data protection. When tested on the NSL-KDD dataset, our framework achieves 98.79% accuracy, a 10% false alarm rate, and a 98.99% detection rate, surpassing existing methods. The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

本文解决了在线社交网络（OSNs）中隐私的关键挑战，其中集中式设计损害了用户隐私。我们提出了一种新的隐私保护框架，该框架将区块链技术与深度学习相结合，以克服这些漏洞。我们的方法采用两层架构：第一层使用精英增强型粒子群优化和引力搜索算法（ePSOGSA）来优化特征选择，而第二层使用增强型非对称深度自动编码器（e-NDAE）进行异常检测。此外，区块链网络通过智能合约保护用户数据，确保强大的数据保护。在NSL-KDD数据集上进行测试时，我们的框架达到了98.79%的准确率，10%的误报率和98.99%的检测率，超过了现有的方法。b区块链与深度学习的融合，不仅增强了osn的隐私保护，还为其他需要健壮安全措施的应用提供了可扩展的模型。

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引用次数: 0

A blockchain-based platform for incentivizing customer reviews in the grocery industry 一个基于区块链的平台，用于激励杂货行业的客户评论

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-12-01 DOI: 10.1016/j.bcra.2024.100226

Tania Bruno , Ettore Etenzi , Luca Gualandi , Eraldo Katra , Rosario Pugliese , Alessio Taranto , Francesco Tiezzi

Nowadays, user-generated content is pivotal for many companies: people trust other customers' opinions more than any brand advertisement. Brands are aware of this and try to promote and motivate their customers to create high-quality content. However, this way of operating is still at an early stage: there is a lack of fairness, as companies typically do not provide a validation system, or if they do, it is not based on a transparent solution, and often, there is no reward for creating unique and high-quality content. In this paper, we focus on the problem of incentivizing users' creation of content in the form of customer reviews in the online grocery industry. Specifically, we illustrate the solution to the problem devised in the Re-Taled project by relying on blockchain technology. We develop a decentralized ecosystem of consumers, influencers, and manufacturers, where content creators are rewarded for their contribution according to a framework that provides incentives in the form of both reputation and monetization. Blockchain technology is used to certify the content's authenticity and compensate content creators with a cryptographic token. We illustrate the technical choices of the solution together with its software architecture and implemented platform. In particular, we introduce the framework used to validate the trustworthiness of user-generated content and favor fairness and transparency within the platform.

如今，用户生成的内容对许多公司来说至关重要：人们更相信其他客户的意见，而不是任何品牌广告。品牌意识到了这一点，并试图促进和激励他们的客户创造高质量的内容。然而，这种运作方式仍处于早期阶段：缺乏公平性，因为公司通常不提供验证系统，或者即使提供验证系统，也不是基于透明的解决方案，而且通常，创造独特和高质量的内容也没有奖励。在本文中，我们专注于在线杂货行业中以客户评论的形式激励用户创建内容的问题。具体来说，我们通过依赖区块链技术来说明re - tale项目中设计的问题的解决方案。我们开发了一个由消费者、影响者和制造商组成的分散生态系统，在这个生态系统中，内容创作者的贡献会根据一个框架得到奖励，该框架以声誉和货币化的形式提供激励。区块链技术用于证明内容的真实性，并用加密令牌补偿内容创建者。我们说明了解决方案的技术选择及其软件体系结构和实现平台。特别是，我们引入了用于验证用户生成内容可信度的框架，并支持平台内的公平性和透明度。

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引用次数: 0

Unraveling the potential of blockchain technology in enhancing supply chain traceability: A systematic literature review and modeling with ISM 揭示区块链技术在提高供应链可追溯性方面的潜力：系统的文献综述和ISM建模

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-11-13 DOI: 10.1016/j.bcra.2024.100240

Reza Payandeh , Ahmad Delbari , Fatemeh Fardad , Javad Helmzadeh , Sanaz Shafiee , Ali Rajabzadeh Ghatari

Supply chain traceability is a critical aspect of modern business operations, and blockchain technology has emerged as a promising solution to enhance traceability in supply chain management. However, the effective application of blockchain faces various challenges and limitations. This study aims to investigate how blockchain technology can address these challenges and improve traceability within supply chains. Employing a systematic literature review combined with interpretative structural modeling (ISM), we comprehensively assess and classify the literature on blockchain-enabled supply chain traceability. Our exploratory research approach delves into the contributions of blockchain technology and identifies key factors that enhance traceability. We adopt a mixed-methods approach, incorporating both secondary and primary data to ensure robust analysis. Our study addresses essential questions regarding the application, advantages, limitations, challenges, integration with other technologies, and future potential of blockchain in supply chain traceability. Through a systematic review and the ISM technique, we identify crucial levels and factors necessary for leveraging blockchain technology effectively. Our findings underscore the importance of a robust infrastructure, cutting-edge technology, and significant initial investment in implementing blockchain for supply chain traceability. This research offers a comprehensive understanding of the factors and their levels, providing valuable insights for industry professionals and academic researchers. By laying a solid foundation for informed decision-making and further exploration into the potential of blockchain-enhanced supply chain traceability, our study contributes to advancing knowledge in this crucial area of business operations.

供应链可追溯性是现代商业运作的一个重要方面，区块链技术已经成为提高供应链管理可追溯性的一个有前途的解决方案。然而，区块链的有效应用面临着各种挑战和限制。本研究旨在探讨区块链技术如何应对这些挑战，并改善供应链中的可追溯性。通过结合解释结构建模（ISM）的系统文献综述，我们对区块链支持的供应链可追溯性的文献进行了全面评估和分类。我们的探索性研究方法深入研究了区块链技术的贡献，并确定了增强可追溯性的关键因素。我们采用混合方法，结合二级和初级数据，以确保稳健的分析。我们的研究解决了关于区块链在供应链可追溯性中的应用、优势、限制、挑战、与其他技术的集成以及未来潜力的基本问题。通过系统回顾和ISM技术，我们确定了有效利用区块链技术所需的关键水平和因素。我们的研究结果强调了强大的基础设施、尖端技术和在实施区块链以实现供应链可追溯性方面的重大初始投资的重要性。这项研究提供了对这些因素及其水平的全面了解，为行业专业人士和学术研究人员提供了有价值的见解。通过为明智的决策奠定坚实的基础，并进一步探索区块链增强供应链可追溯性的潜力，我们的研究有助于提高这一关键业务运营领域的知识。

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引用次数: 0

Enabling efficient verification in a DApp: The case of copyright management 在DApp中实现高效验证：以版权管理为例

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-10-10 DOI: 10.1016/j.bcra.2024.100234

Pierpaolo Della Monica , Matteo Fedeli , Cristina Salonico , Andrea Vitaletti , Marco Zecchini

The Interested Party Information (IPI) system uniquely identifies the rights holders worldwide, making it possible to know for each subject and at any time which rights are protected, by whom and for which territories. Currently, this service is provided in a centralized way but in 2021, the Italian Society of Authors and Editors (SIAE) deployed a blockchain-based solution to completely decentralize this database to (a) provide greater guarantees to the rights holders as well as end users and (b) make a first tangible step in the path towards an all in-chain solution decentralizing a relevant component of the current architecture. This solution relied on early versions of Algorand smart contracts, delegating some off-chain verification to trusted third parties in many practical scenarios. Moreover, the Algorand technology has developed new tools, allowing us to design new techniques to reduce some of the trust assumptions of the original solution and enhance its efficiency at the same time. In this paper, we present the evolution of the solutions we designed to issue new on-chain non-conflicting rights representations, namely representations that are consistent with those already available on-chain. Our solution relies on smart contracts that have been implemented to run our experiments to prove (a) the feasibility of the proposed approach, (b) the scalability of the proposed solutions, and (c) the sustainability in terms of costs.

利害关系方信息（IPI）系统唯一地标识全世界的权利持有人，从而可以随时了解每个主体的哪些权利受到谁的保护以及在哪些地区受到保护。目前，这项服务是以集中的方式提供的，但在2021年，意大利作者和编辑协会（SIAE）部署了一个基于区块链的解决方案，以完全分散该数据库，以便(a)为权利持有人和最终用户提供更大的保证，以及(b)朝着分散当前架构相关组件的全链解决方案迈出切实的第一步。该解决方案依赖于早期版本的Algorand智能合约，在许多实际场景中将一些链下验证委托给受信任的第三方。此外，Algorand技术开发了新的工具，允许我们设计新的技术来减少原始解决方案的一些信任假设，同时提高其效率。在本文中，我们介绍了我们设计的解决方案的演变，以发布新的链上无冲突的权利表示，即与链上已有的权利表示一致。我们的解决方案依赖于已经实施的智能合约来运行我们的实验，以证明(a)拟议方法的可行性，(b)拟议解决方案的可扩展性，以及(c)成本方面的可持续性。

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引用次数: 0

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis 利用机器学习分类器和可解释性分析检测区块链交易中的异常情况

IF 6.9 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Blockchain-Research and Applications

Pub Date : 2024-09-01 DOI: 10.1016/j.bcra.2024.100207

Mohammad Hasan , Mohammad Shahriar Rahman , Helge Janicke , Iqbal H. Sarker

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.

随着区块链在数字支付领域的应用不断增加，它也容易受到各种恶意攻击。成功检测区块链交易中的异常情况对于增强数字支付的信任度至关重要。然而，由于非法交易很少发生，在区块链交易数据中进行异常检测是一项具有挑战性的任务。虽然该领域已开展了多项研究，但仍存在一个局限性：缺乏对模型预测的解释。本研究试图通过将可解释人工智能（XAI）技术和异常规则整合到基于树的集合分类器中来克服这一局限，以检测异常比特币交易。我们采用夏普利加法解释（SHAP）方法来衡量每个特征的贡献，该方法与集合模型兼容。此外，我们还提出了解释比特币交易是否异常的规则。此外，我们还引入了一种名为 XGBCLUS 的低采样算法，旨在平衡异常和非异常交易数据。我们将该算法与其他常用的低采样和高采样技术进行了比较。最后，将各种基于树的单一分类器的结果与堆叠和投票集合分类器的结果进行了比较。实验结果表明(i) 与最先进的欠采样和过采样技术相比，XGBCLUS 提高了真阳性率（TPR）和接收者操作特征曲线下面积（ROC-AUC）分数；(ii) 我们提出的集合分类器在准确率、TPR 和假阳性率（FPR）分数方面优于传统的基于树的单一机器学习分类器。

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引用次数: 0