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一种基于决策树的比特币不可达节点发现方法

李锐光 朱佳伟 吴阜东 高家奇 徐大伟 祝烈煌

李锐光,朱佳伟,吴阜东,等. 一种基于决策树的比特币不可达节点发现方法[J]. 北京航空航天大学学报,2024,50(6):1861-1867 doi: 10.13700/j.bh.1001-5965.2022.0558
引用本文: 李锐光,朱佳伟,吴阜东,等. 一种基于决策树的比特币不可达节点发现方法[J]. 北京航空航天大学学报,2024,50(6):1861-1867 doi: 10.13700/j.bh.1001-5965.2022.0558
LI R G,ZHU J W,WU F D,et al. A decision tree-based discovery method for Bitcoin unreachable nodes[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(6):1861-1867 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0558
Citation: LI R G,ZHU J W,WU F D,et al. A decision tree-based discovery method for Bitcoin unreachable nodes[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(6):1861-1867 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0558

一种基于决策树的比特币不可达节点发现方法

doi: 10.13700/j.bh.1001-5965.2022.0558
基金项目: 国家重点研发计划(2020YFB1006100); 国家自然科学基金(62106060)
详细信息
    通讯作者:

    E-mail:liehuangz@bit.edu.cn

  • 中图分类号: TP393;TN919

A decision tree-based discovery method for Bitcoin unreachable nodes

Funds: National Key R & D Program of China (2020YFB1006100); National Natural Science Foundation of China (62106060)
More Information
  • 摘要:

    不可达节点是指比特币网络中不接收外部连接请求的网络工作节点,发现、验证均较为困难。现有研究大多集中于可达节点,而对不可达节点的研究较少。为此,提出一种基于决策树算法的不可达节点发现方法,可以从大量比特币地址中自动分类发现不可达节点。实验结果表明:所提方法在实验数据集上分类准确率为95.73%,召回率为91.97%;在真实数据上进行实测,并利用网络空间搜索引擎进行验证,所提方法实际分类准确率为53.75%,召回率约为76.86%。对实验中发现不可达节点的总量、地理分布、所属网络服务商等进行统计分析,为比特币监管工作提供有力技术支撑。

     

  • 图 1  BNS系统结构

    Figure 1.  System structure of BNS

    图 2  比特币地址分类

    Figure 2.  Classification of Bitcoin addresses

    表  1  决策树深度与分类效果

    Table  1.   Decision tree depth and classification effect

    树深度准确率/%召回率/%F1
    185.8499.260.8625
    288.8694.540.8836
    389.1097.200.8887
    492.8595.390.9227
    595.7291.980.9506
    1097.9995.930.9771
    1599.3298.830.9924
    2099.7399.570.9969
    2299.7699.660.9973
    2599.7599.640.9972
    3099.7599.640.9972
    下载: 导出CSV

    表  2  节点最小样本数与分类效果

    Table  2.   Node's minimum sample and classification effect

    最小样本数 准确率/% 召回率/% F1
    100 95.72 91.98 0.9506
    200 95.73 91.97 0.9381
    300 95.67 92.19 0.9501
    400 95.61 9.17 0.9492
    500 95.44 91.27 0.9472
    600 95.27 92.15 0.9458
    下载: 导出CSV

    表  3  优化前后分类效果

    Table  3.   Classification effect before and after optimization

    分类算法 准确率/% 召回率/% F1
    决策树(默认参数) 85.84 86.67 0.8625
    决策树(优化后) 95.73 91.97 0.9381
    下载: 导出CSV

    表  4  检索记录与相应的节点类型

    Table  4.   Searching records and corresponding node types

    检索
    记录
    可达
    节点
    不可达
    节点
    离线可达
    节点
    离线不可达
    节点
    虚假
    节点
    是否可以
    检索到记录
    是否提供
    比特币服务
    是否超出
    扫描周期
    下载: 导出CSV

    表  5  真实数据分类

    Table  5.   Real data classification

    可达节点 不可达节点 离线可达节点 离线不可达节点 虚假节点 总计
    0 5688 113 2338 2444 10583
    下载: 导出CSV

    表  6  分类效果

    Table  6.   Classification results

    数据集 准确率/% 召回率/% F1
    实验数据集 95.73 91.97 0.9381
    真实数据 53.75 76.86 0.6326
    下载: 导出CSV

    表  7  节点地理分布

    Table  7.   Geographical distribution of nodes

    地区 所占比例/%
    可达节点[4] 不可达节点
    欧洲 58.07 50.21
    美洲 30.91 29.91
    亚洲 9.43 18.18
    大洋洲 1.36 1.16
    非洲 0.22 0.54
    下载: 导出CSV

    表  8  所属网络服务商统计

    Table  8.   Statistics of networkservice providers

    网络服务商 不可达节点数量 占比/%
    amazon.com 372 7.3
    hetzner.com 293 5.7
    digitalocean.com 276 5.4
    China Telecom 178 3.5
    comcast.com 162 3.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-06-30
  • 录用日期:  2022-08-26
  • 网络出版日期:  2022-12-08
  • 整期出版日期:  2024-06-27

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