HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering
Source abstract
Abstract
In this paper, we predict money laundering in Bitcoin transactions by leveraging a deep learning framework and incorporating more characteristics of Bitcoin transactions. We produced a dataset containing 46,045 Bitcoin transaction entities and 319,311 Bitcoin wallet addresses associated with them. We aggregated this information to form a heterogeneous graph dataset and propose three metapath representations around transaction entities, which enrich the characteristics of Bitcoin transactions. Then, we designed a metapath encoder and integrated it into a heterogeneous graph node embedding method. The experimental results indicate that our proposed framework significantly improves the accuracy of illicit Bitcoin transaction recognition compared with traditional methods. Therefore, our proposed framework is more conducive in detecting money laundering activities in Bitcoin transactions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Crime, Illicit Activities, and Governance · Blockchain Technology Applications and Security · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Network Security, People’s Public Security University of China, Beijing 100032, China
Funding
Double First-Class Innovation Research Project for the People’s Public Security University of China
References (27)
Show source-provided references
1. Countering money laundering and terrorist financing: A case for bitcoin regulation10.1016/j.ribaf.2021.101387 ↗
2. Jiang, M., Liu, G., Su, Y., and Wu, X. (2021). GCN-SL: Graph Convolutional Networks with Structure Learning for Graphs under Heterophily. arXiv.
3. Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., and He, Q. (2021, January 19–23). Pick and choose: A GNN-based imbalanced learning approach for fraud detection. Proceedings of the Web Conference 2021, Ljubljana, Slovenia.10.1145/3442381.3449989 ↗
4. Claudio Bellei, A. (2023, July 26). The Elliptic Data Set: Opening up Machine Learning on the blockchain.Medium (Aug.2019). Available online: https://medium.com/elliptic/the-elliptic-data-set-opening-up-machine-learning-on-the-blockchain-e0a343d99a14.
5. Translating embeddings for modeling multi-relational data
6. Fu, X., Zhang, J., Meng, Z., and King, I. (2020, January 20–24). Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. Proceedings of the Web Conference 2020, Taipei, Taiwan.10.1145/3366423.3380297 ↗
7. Platt, J. (2023, July 26). Sequential Minimal Optimization: A Fast Algorithm for Training. Available online: https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/.
8. Random forests10.1023/A:1010933404324 ↗
9. Chen, T., and Guestrin, C. (2016, January 13–17). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939785 ↗
10. Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T., and Leiserson, C.E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv.
11. Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv.
12. Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., and Leiserson, C. (2020, January 7–12). Evolvegcn: Evolving graph convolutional networks for dynamic graphs. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i04.5984 ↗
13. Convolutional LSTM network: A machine learning approach for precipitation nowcasting
14. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.10.3115/v1/D14-1179 ↗
15. Hu, Y., Seneviratne, S., Thilakarathna, K., Fukuda, K., and Seneviratne, A. (2019). Characterizing and detecting money laundering activities on the bitcoin network. arXiv.
16. Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24–27). Deepwalk: Online learning of social representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.10.1145/2623330.2623732 ↗
17. Grover, A., and Leskovec, J. (2016, January 13–17). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939754 ↗
18. Lorenz, J., Silva, M.I., Aparício, D., Ascensão, J.T., and Bizarro, P. (2020, January 15–16). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. Proceedings of the First ACM International Conference on AI in Finance, New York, NY, USA.10.1145/3383455.3422549 ↗
19. Oliveira, C., Torres, J., Silva, M.I., Aparício, D., Ascensão, J.T., and Bizarro, P. (2021). GuiltyWalker: Distance to illicit nodes in the Bitcoin network. arXiv.
20. Qualifying and raising anti-money laundering alarms with deep learning10.1016/j.eswa.2022.119037 ↗
21. Anti-money Laundering Supervision by Intelligent Algorithm10.1016/j.cose.2023.103344 ↗
22. Zhao, K., Dong, G., and Bian, D. (2023). Detection of Illegal Transactions of Cryptocurrency Based on Mutual Information. Electronics, 12.10.3390/electronics12071542 ↗
23. Xiang, Y., Ren, W., Gao, H., Bao, D., Lei, Y., Li, T., Yang, Q., Liu, W., Zhu, T., and Choo, K.K.R. (2022). BABD: A Bitcoin Address Behavior Dataset for Address Behavior Pattern Analysis. arXiv.
24. Alexander (2023, July 26). De-Anonymization of Elliptic Dataset Transactions. Available online: https://habr.com/ru/post/479178/.
25. Mining heterogeneous information networks: A structural analysis approach10.1145/2481244.2481248 ↗
26. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv.
27. Scikit-learn: Machine learning in Python