信息网络安全

信息网络安全 ›› 2026, Vol. 26 ›› Issue (3): 482-490.doi: 10.3969/j.issn.1671-1122.2026.03.014

• 入选论文 • 上一篇    下一篇

区块链虚拟货币溯源与分析

梁广俊1(), 裘宇辰1, 司宏韬2, 王群1, 马卓1, 陈宇琪1   

  1. 1.江苏警官学院计算机信息与网络安全系，南京 210031
    2.南京市公安局江北新区分局，南京 211800
  • 收稿日期:2025-07-28 出版日期:2026-03-10 发布日期:2026-03-30
  • 通讯作者: 梁广俊 E-mail:lianggjun@126.com
  • 作者简介:梁广俊（1982—），男，安徽，副教授，博士，CCF会员，主要研究方向为网络空间安全|裘宇辰（2002—），男，江苏，本科，主要研究方向为网络空间安全|司宏韬（1986—），男，辽宁，本科，主要研究方向为网络空间安全|王群（1971—），男，甘肃，教授，博士，CCF杰出会员，主要研究方向为网络空间安全|马卓（1993—），女，山西，讲师，博士，CCF会员，主要研究方向为信息安全|陈宇琪（1991—），女，江苏，讲师，硕士，主要研究方向为网络空间安全
  • 基金资助:
    国家自然科学基金(62202209)

Blockchain Virtual Currency Traceability and Analysis

LIANG Guangjun1(), QIU Yuchen1, SI Hongtao2, WANG Qun1, MA Zhuo1, CHEN Yuqi1   

  1. 1. Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing 210031, China
    2. Nanjing Public Security Bureau Jiangbei New District Sub-Bureau, Nanjing 211800, China
  • Received:2025-07-28 Online:2026-03-10 Published:2026-03-30

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摘要：

虚拟货币的快速发展和交易规模激增为洗钱和电信诈骗等违法犯罪活动提供了隐蔽通道，为金融秩序与公共安全带来了严重威胁。区块链分布式账本具有公开可追溯特性，其链上全量交易数据隐含地址关联拓扑，是锁定涉案钱包、追踪资金流向的核心依据。文章基于图论与数据挖掘技术，设计并实现一套融合交易可视化与异常检测的虚拟货币溯源系统。利用Python与Neo4j构建交易网络模型，通过可视化呈现地址链路，通过k-means算法对地址多维特征进行聚类，利用样本与簇中心欧氏距离识别异常，实现地址属性判别。在动态调整特征维度与权重后，系统成功定位两个已被封禁的地址，验证了该方法的有效性与实用性。

关键词: 区块链, 虚拟货币, 交易溯源, 异常检测, k-means算法

Abstract:

The rapid development and surging transaction scale of virtual currencies have provided covert channels for illegal and criminal activities such as money laundering and fraud, threatening financial order and public security. As a public and traceable distributed ledger, the blockchain contains implicit address association topology in its full-chain transaction data, which can serve as the core basis for identifying involved wallets and tracking fund flows. Based on graph theory and data mining technologies, this paper designed and implemented a virtual currency traceability system integrating transaction visualization and anomaly detection. A transaction network model was constructed using Python and Neo4j to visually present address links. The k-means algorithm was applied to cluster multi-dimensional features of addresses, and the Euclidean distance between samples and cluster centers was used to identify anomalies, thus realizing the discrimination of address attributes. After dynamically adjusting feature dimensions and weights, the system successfully located 2 blocked addresses, which verifies the effectiveness and practicality of the proposed method.

Key words: blockchain, virtual currency, transaction traceability, anomaly detection, k-means algorithm

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