通过混合区块链可靠性预测选择可靠的区块链同行

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING IET Software Pub Date : 2023-02-11 DOI:10.1049/sfw2.12118
Peilin Zheng, Zibin Zheng, Liang Chen
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引用次数: 8

摘要

区块链和基于区块链的去中心化应用最近越来越受到关注。在公共区块链系统中,用户通常连接到第三方对等体或运行对等体加入P2P区块链网络。然而,连接到不可靠的区块链同行会导致重复交易造成资源浪费,甚至加密货币的损失。为了选择可靠的区块链同行,迫切需要评估和预测他们的可靠性。面对这个问题,我们提出了混合区块链可靠性预测(H-BRP),一种混合区块链可靠度预测模型,以提取区块链可靠性因素,然后为每个用户进行个性化预测。在100个区块链请求者和200个区块链对等体上进行的综合实验证明了所提出的H-BRP模型的有效性。此外,还发布了2000000个测试用例的实现和数据集。
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Selecting reliable blockchain peers via hybrid blockchain reliability prediction

Blockchain and blockchain-based decentralised applications have been attracting increasing attention recently. In public blockchain systems, users usually connect to third-party peers or run a peer to join the P2P blockchain network. However, connecting to unreliable blockchain peers will lead to resource waste and even loss of cryptocurrencies by repeated transactions. In order to select reliable blockchain peers, it is urgently needed to evaluate and predict their reliability of them. Faced with this problem, we propose hybrid blockchain reliability prediction (H-BRP), a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H-BRP model. Further, the implementation and dataset of 2,000,000 test cases are released.

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来源期刊
IET Software
IET Software 工程技术-计算机:软件工程
CiteScore
4.20
自引率
0.00%
发文量
27
审稿时长
9 months
期刊介绍: IET Software publishes papers on all aspects of the software lifecycle, including design, development, implementation and maintenance. The focus of the journal is on the methods used to develop and maintain software, and their practical application. Authors are especially encouraged to submit papers on the following topics, although papers on all aspects of software engineering are welcome: Software and systems requirements engineering Formal methods, design methods, practice and experience Software architecture, aspect and object orientation, reuse and re-engineering Testing, verification and validation techniques Software dependability and measurement Human systems engineering and human-computer interaction Knowledge engineering; expert and knowledge-based systems, intelligent agents Information systems engineering Application of software engineering in industry and commerce Software engineering technology transfer Management of software development Theoretical aspects of software development Machine learning Big data and big code Cloud computing Current Special Issue. Call for papers: Knowledge Discovery for Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_KDSD.pdf Big Data Analytics for Sustainable Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_BDASSD.pdf
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