Blockchain Virtual Currency Traceability and Analysis

doi:10.3969/j.issn.1671-1122.2026.03.014

Abstract

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

CLC Number:

TP309

LIANG Guangjun, QIU Yuchen, SI Hongtao, WANG Qun, MA Zhuo, CHEN Yuqi. Blockchain Virtual Currency Traceability and Analysis[J]. Netinfo Security, 2026, 26(3): 482-490.

Figures/Tables 7

References 18

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