LauHunter：一种快速高效的洗钱检测工具

doi:10.3969/j.issn.0255-8297.2026.05.002

Abstract

Abstract: Existing blockchain risk control methods often struggle to effectively identify suspicious accounts and transaction pathways due to the massive scale of Ethereum network data, sparse label information, and high computational complexity. To address this problem, this paper proposes LauHunter, a novel Ethereum money laundering detection tool that combines reverse clustering analysis with fine-grained behavioral pattern analysis. Experimental results demonstrate that LauHunter significantly outperforms existing mainstream detection methods in both detection accuracy and efficiency, particularly when handling complex fund flow pathways, thus assisting regulatory authorities in tracking illicit fund flows.

Key words: blockchain security, money laundering detection, Ethereum, anomaly detection

CLC Number:

TP309.2

GUO Li, ZHANG Wenjie, PAN Heng, LU Siqi, XING Ying, LI Kunyang, ZHANG Bowei, SI Xueming. LauHunter: A Fast and Efficient Tool for Money Laundering Detection[J]. Journal of Applied Sciences, 2026, 44(5): 716-730.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2026.05.002

https://www.jas.shu.edu.cn/EN/Y2026/V44/I5/716

[1] Wood G. Ethereum: a secure decentralised generalised transaction ledger [J]. Ethereum Project Yellow Paper, 2014, 151: 1-32.
[2] 刘乐源, 李湘叶, 蓝天, 等. 区块链系统中反洗钱技术研究综述[J]. 中国工程科学, 2025, 27(2): 287-303. Liu L Y, Li X Y, Lan T, et al. Review on anti-money laundering technologies in blockchain systems [J]. Strategic Study of CAE, 2025, 27(2): 287-303. (in Chinese)
[3] 李广, 陈梓钿, 卞静, 等. 区块链欺诈行为识别技术综述[J]. 信息安全学报, 2024, 9(4): 1-30. Li G, Chen Z D, Bian J, et al. Blockchain fraud behaviors detection technology: a survey [J]. Journal of Cyber Security, 2024, 9(4): 1-30. (in Chinese)
[4] 王燕, 潘振生, 贾林鹏. 区块链3.0时代虚拟货币洗钱犯罪情报分析研究[J]. 情报杂志, 2024, 43(8): 59-65. Wang Y, Pan Z S, Jia L P. Intelligence analysis of virtual currency money laundering crimes in the blockchain 3.0 era [J]. Journal of Intelligence, 2024, 43(8): 59-65. (in Chinese)
[5] 王佳鑫, 颜嘉麒, 毛谦昂. 加密数字货币监管技术研究综述[J]. 计算机应用, 2023, 43(10): 2983- 2995. Wang J X, Yan J Q, Mao Q A. Review on regulatory technologies of cryptocurrency [J]. Journal of Computer Applications, 2023, 43(10): 2983-2995. (in Chinese)
[6] Victor F. Address clustering heuristics for ethereum [C]//24th International Conference Financial Cryptography and Data Security, 2020: 617-633.
[7] Zhang Y, Wang J, Luo J. Heuristic-based address clustering in bitcoin [J]. IEEE Access, 2020, 8: 210582-210591.
[8] Chaudhari D, Agarwal R, Shukla S K. Towards malicious address identification in bitcoin [C]//2021 IEEE International Conference on Blockchain, 2021: 425-432.
[9] Liu J, Zheng W, Lu D, et al. From decentralization to oligopoly: a data-driven analysis of decentralization evolution and voting behaviors on EOSIO [J]. IEEE Transactions on Computational Social Systems, 2022, 10(5): 2752-2763.
[10] Yu C, Jiang C, Yang C, et al. Stealthy clustering of ethereum transactions using time leakage from fixed nodes [C]//2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops, 2022: 7-12.
[11] Li M, Cui B, Hou W, et al. Research on malicious account detection mechanism of ethereum based on community discovery [C]//2023 IEEE 47th Annual Computers, Software, and Applications Conference, 2023: 705-710.
[12] Wang M, Ichijo H, Xiao B. Cryptocurrency address clustering and labeling [PP/OL]. V1(2020- 03-30) [2026-01-30]. https://doi.org/10.48550/arXiv. 2003.13399.
[13] Rajput Q, Khan N S, Larik A, et al. Ontology based expert-system for suspicious transactions detection [J]. Computer and Information Science, 2014, 7(1): 103.
[14] Khanuja H K, Adane D S. Forensic analysis for monitoring database transactions [C]//International Symposium on Security in Computing and Communication, 2014: 201-210.
[15] Farrugia S, Ellul J, Azzopardi G. Detection of illicit accounts over the ethereum blockchain [J]. Expert Systems with Applications, 2020, 150: 113318.
[16] Yu Y, Wu J, Lin D, et al. Money laundering detection on ethereum: applying traditional approaches to new scene [C]//2023 IEEE 29th International Conference on Parallel and Distributed Systems, 2023: 1759-1766.
[17] Tharani J S, Hóu Z, Charles E Y A, et al. Unified feature engineering for detection of malicious entities in blockchain networks [J]. IEEE Transactions on Information Forensics and Security, 2024, 19(9): 8924-8938.
[18] Li X, Liu S, Li Z, et al. Flowscope: spotting money laundering based on graphs [C]//The 34th AAAI Conference on Artificial Intelligence, 2020: 4731-4738.
[19] Lin D, Wu J, Yu Y, et al. DenseFlow: spotting cryptocurrency money laundering in ethereum transaction graphs [C]//ACM Web Conference, 2024: 4429-4438.
[20] Song J, Zhang S, Zhang P, et al. Illicit social accounts? anti-money laundering for transactional blockchains [J]. IEEE Transactions on Information Forensics and Security, 2024, 20: 391-404.
[21] Yu L, Zhang F, Ma J, et al. Who are the money launderers? money laundering detection on blockchain via mutual learning-based graph neural network [C]//International Joint Conference on Neural Networks, 2023: 1-8.
[22] Liu J, Yin C, Wang H, et al. Graph embedding-based money laundering detection for ethereum [J]. Electronics, 2023, 12(14): 3180.
[23] Liu J, Chen J, Wu J, et al. Fishing for fraudsters: uncovering ethereum phishing gangs with blockchain data [J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 3038- 3050.
[24] Sun X, Feng W, Liu S, et al. MonLAD: money laundering agents detection in transaction streams [C]//15th ACM International Conference on Web Search and Data Mining, 2022: 976-986.
[25] Wu J, Lin D, Fu Q, et al. Toward understanding asset flows in crypto money laundering through the lenses of ethereum heists [J]. IEEE Transactions on Information Forensics and Security, 2023, 19: 1994-2009.
[26] Hu S, Zhang Z, Luo B, et al. Bert4eth: a pre-trained transformer for ethereum fraud detection [C]//ACM Web Conference, 2023: 2189-2197.
[27] Yang J, Yu W, Wu J, et al. 2DynEthNet: a two-dimensional streaming framework for ethereum phishing scam detection [J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 9924-9937.
[28] Hou W, Cui B, Chen Y, et al. TSFF: a triple-stream feature fusion method for ethereum phishing scam detection [J]. IEEE Internet of Things Journal, 2024, 12(3): 2623-2632.
[29] Li S, Gou G, Liu C, et al. TGC: transaction graph contrast network for ethereum phishing scam detection [C]//39th Annual Computer Security Applications Conference, 2023: 352-365.
[30] Lin D, Wu J, Fu Q, et al. RiskProp: account risk rating on Ethereum via de-anonymous score and network propagation [J]. IEEE Transactions on Dependable and Secure Computing, 2025, 22(3): 2132-2145.
[31] Akoglu L, Chandy R, Faloutsos C. Opinion fraud detection in online reviews by network effects [C]//International AAAI Conference on Web and Social Media, 2013, 7(1): 2-11.
[32] Hooi B, Shah N, Beutel A, et al. Birdnest: Bayesian inference for ratings-fraud detection [C]//2016 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 2016: 495-503.

LauHunter: A Fast and Efficient Tool for Money Laundering Detection

PDF

Knowledge

Abstract

Cite this article

share this article

Related Articles 9

Recommended Articles

Metrics

Comments

[1]	WU Xiang, XIAO Jian, JI Genlin. Video Anomaly Detection Method Based on Multi-scale Feature Fusion and Attention Mechanism [J]. Journal of Applied Sciences, 2025, 43(2): 234-244.
[2]	FEI Jiajia, ZHAO Xiangfu, CHEN Xiaohan, ZHANG Dengji. Smart Contract Vulnerability Analysis and Improvement Based on Smartcheck [J]. Journal of Applied Sciences, 2024, 42(6): 1027-1039.
[3]	WANG Zexu, WEN Bin. Smart Contract Vulnerability Detection of Symbol Execution with Critical Path Pre-searching [J]. Journal of Applied Sciences, 2024, 42(2): 364-374.
[4]	ZENG Jing, LI Ying, QI Xiaosha, JI Genlin. Video Anomaly Detection Method Based on Secondary Prediction of Multi-layer Memory Enhancement Generative Adversarial Network [J]. Journal of Applied Sciences, 2023, 41(1): 80-94.
[5]	CHEN Xiaohan, ZHAO Xiangfu, ZHANG Dengji, FEI Jiajia. SlightDetection: A Static Analysis Tool for Smart Contracts Security Vulnerabilities on Ethereum [J]. Journal of Applied Sciences, 2022, 40(4): 695-712.
[6]	ZHANG Dengji, ZHAO Xiangfu, CHEN Zhongyu, TONG Xiangrong. Analysis of Security Strategies for Smart Contracts Based on Ethereum [J]. Journal of Applied Sciences, 2021, 39(1): 151-163.
[7]	YU Yue, DENG Li, PANG Hong-lin, FEI Min-rui. Environmental Anomaly Detection Method during Crop Growth Based on Distributed Clustering [J]. Journal of Applied Sciences, 2018, 36(6): 1010-1021.
[8]	CHENG Bao-zhi, ZHAO Chun-hui, WANG Yu-lei. SVDD Algorithm with Spectral Unmixing for Anomaly Detection in Hyperspectral Images [J]. Journal of Applied Sciences, 2012, 30(1): 82-88.
[9]	TIAN Xing－uang;DUAN Mi-yi;SUN Chun-lai;LI Wen-fa . Detection of Anomalous User Behavior Based on Shell Commands and Hidden Markov Models [J]. Journal of Applied Sciences, 2008, 26(2): 175-175 .