How to cite this paper
Alsweed, R & Ibrahim, D. (2026). Detecting bitcoin fraud using graph neural networks.International Journal of Data and Network Science, 10(2), 537-546.
References
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Chen, B., Wei, F., & Gu, C. (2021). Bitcoin theft detection based on supervised machine learning algorithms. Security and Communication Networks, 2021, 6643763. https://doi.org/10.1155/2021/6643763
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Duan, X., Yan, B., Dong, A., Zhang, L., & Yu, J. (2022). Phishing frauds detection based on graph neural network on Ethereum. In Lecture Notes in Computer Science, 13426, pp. 351–363). https://doi.org/10.1007/978-3-031-19208-1_29
Elmougy, Y., & Liu, L. (2023). Demystifying fraudulent transactions and illicit nodes in the Bitcoin network for financial forensics. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 29, pp. 3979–3990). https://doi.org/10.1145/3580305.3599803
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Hajek, P., Abedin, M. Z., & Sivarajah, U. (2023). Fraud detection in mobile payment systems using an XGBoost-based framework. Information Systems Frontiers, 25, 1985–2003. https://doi.org/10.1007/s10796-022-10346-6
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning (ICML), 37, pp. 448–456). https://doi.org/10.48550/arXiv.1502.03167
Iqbal, M. S., Zulkernine, M., Jaafar, F., & Gu, Y. (2016). FCFraud: Fighting click-fraud from the user side. In Proceedings of the IEEE International Symposium on High Assurance Systems Engineering, 17, pp. 157–164). https://doi.org/10.1109/HASE.2016.17
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245. https://doi.org/10.1016/j.eswa.2018.01.037
Kim, E., Lee, J., Shin, H., Yang, H., Cho, S., Nam, S. K., Song, Y., Yoon, J., & Kim, J. (2019). Champion–challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning. Expert Systems with Applications, 128, 214–224. https://doi.org/10.1016/j.eswa.2019.03.042
Lee, C., Maharjan, S., Ko, K., & Jang, J. W. K. (2020). Toward detecting illegal transactions on Bitcoin using machine-learning methods. In Communications in Computer and Information Science, 1156, pp. 520–533).
Liu, L., Tsai, W. T., Bhuiyan, M. Z. A., Peng, H., & Liu, M. (2022). Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum. Future Generation Computer Systems, 128, 158–166.
Lucas, Y., Portier, P. E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393–402.
Nerurkar, P., Bhirud, S., Patel, D., Ludinard, R., Busnel, Y., & Kumari, S. (2021a). Supervised learning model for identifying illegal activities in Bitcoin. Applied Intelligence, 51, 3824–3843. https://doi.org/10.1007/s10489-020-02048-w
Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., & Khan, M. K. (2021b). Dissecting Bitcoin blockchain: Empirical analysis of the Bitcoin network (2009–2020). Journal of Network and Computer Applications, 177, 102940. https://doi.org/10.1016/j.jnca.2020.102940
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., & Leiserson, C. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, 34, pp. 5363–5370). https://doi.org/10.1609/aaai.v34i04.5984
Patel, V., Pan, L., & Rajasegarar, S. (2020). Graph deep learning-based anomaly detection in Ethereum blockchain network. In Lecture Notes in Computer Science, 12496, pp. 132–148).
Santos, L. J. S., & Ocampo, S. R. (2018). Bayesian method with clustering algorithm for credit card transaction fraud detection. Romanian Statistical Review, 66, 103–120.
Song, A., Seo, E., & Kim, H. (2023). Anomaly VAE-Transformer: A deep learning approach for anomaly detection in decentralized finance. IEEE Access, 11, 1–15. https://doi.org/10.1109/ACCESS.2023.3313448
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958. https://doi.org/10.48550/arXiv.1207.0580
Sun, H., Li, J., & Zhu, X. (2023). Financial fraud detection based on the part-of-speech features of textual risk disclosures in financial reports. Procedia Computer Science, 221, 57–64. https://doi.org/10.1016/j.procs.2023.07.009
Tan, R., Tan, Q., Zhang, P., & Li, Z. (2021). Graph neural network for Ethereum fraud detection. In Proceedings of the IEEE International Conference on Big Knowledge (ICBK) (pp. 78–85).
Tian, H., Li, Y., Cai, Y., Shi, X., & Zheng, Z. (2021). Attention-based graph neural network for identifying illicit Bitcoin addresses. In Communications in Computer and Information Science, 1490, pp. 147–162). https://doi.org/10.1007/978-981-16-7993-3_11
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection: Machine learning methods. Proceedings of the International Symposium INFOTEH-JAHORINA, 18, 1–5. https://doi.org/10.1109/INFOTEH.2019.8717766
Vujicic, D., Jagodic, D., & Randic, S. (2018). Blockchain technology, Bitcoin, and Ethereum: A brief overview. Proceedings of the International Symposium INFOTEH-JAHORINA, 17, 1–6. https://doi.org/10.1109/INFOTEH.2018.8345547
Xiuguo, W., & Shengyong, D. (2022). An analysis on financial statement fraud detection for Chinese listed companies using deep learning. IEEE Access, 10, 22516–22532. https://doi.org/10.1109/ACCESS.2022.3153478
Yu, C., Zuo, Y., Feng, B., An, L., & Chen, B. (2019). An individual–group–merchant relation model for identifying fake online reviews: An empirical study on a Chinese e-commerce platform. Information Technology and Management, 20, 123–138. https://doi.org/10.1007/s10799-018-0288-1
Zheng, Y. (2022). GRU-GAT model for blockchain Bitcoin abnormal transaction detection. In Proceedings of the IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) (pp. 666–674). https://doi.org/10.1109/TOCS56154.2022.10016137
Berrar, D. (2016). Learning from automatically labeled data: Case study on click fraud prediction. Knowledge and Information Systems, 46, 477–490. https://doi.org/10.1007/s10115-015-0827-6
Chainalysis. (2024, January). 2024 crypto crime trends: Illicit activity down as scamming and stolen funds fall, but ransomware and darknet markets see growth. Chainalysis Blog. Retrieved July 2, 2025, from https://www.chainalysis.com/blog/2024-crypto-crime-report-introduction/
Chen, B., Wei, F., & Gu, C. (2021). Bitcoin theft detection based on supervised machine learning algorithms. Security and Communication Networks, 2021, 6643763. https://doi.org/10.1155/2021/6643763
Deng, R., Ruan, N., Zhang, G., & Zhang, X. (2020). FraudJudger: Fraud detection on digital payment platforms with fewer labels. In Lecture Notes in Computer Science, 11999, pp. 569–583). https://doi.org/10.1007/978-3-030-41579-2_33
Duan, X., Yan, B., Dong, A., Zhang, L., & Yu, J. (2022). Phishing frauds detection based on graph neural network on Ethereum. In Lecture Notes in Computer Science, 13426, pp. 351–363). https://doi.org/10.1007/978-3-031-19208-1_29
Elmougy, Y., & Liu, L. (2023). Demystifying fraudulent transactions and illicit nodes in the Bitcoin network for financial forensics. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 29, pp. 3979–3990). https://doi.org/10.1145/3580305.3599803
Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455. https://doi.org/10.1016/j.ins.2017.12.030
Hajek, P., Abedin, M. Z., & Sivarajah, U. (2023). Fraud detection in mobile payment systems using an XGBoost-based framework. Information Systems Frontiers, 25, 1985–2003. https://doi.org/10.1007/s10796-022-10346-6
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the International Conference on Machine Learning (ICML), 37, pp. 448–456). https://doi.org/10.48550/arXiv.1502.03167
Iqbal, M. S., Zulkernine, M., Jaafar, F., & Gu, Y. (2016). FCFraud: Fighting click-fraud from the user side. In Proceedings of the IEEE International Symposium on High Assurance Systems Engineering, 17, pp. 157–164). https://doi.org/10.1109/HASE.2016.17
Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245. https://doi.org/10.1016/j.eswa.2018.01.037
Kim, E., Lee, J., Shin, H., Yang, H., Cho, S., Nam, S. K., Song, Y., Yoon, J., & Kim, J. (2019). Champion–challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning. Expert Systems with Applications, 128, 214–224. https://doi.org/10.1016/j.eswa.2019.03.042
Lee, C., Maharjan, S., Ko, K., & Jang, J. W. K. (2020). Toward detecting illegal transactions on Bitcoin using machine-learning methods. In Communications in Computer and Information Science, 1156, pp. 520–533).
Liu, L., Tsai, W. T., Bhuiyan, M. Z. A., Peng, H., & Liu, M. (2022). Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum. Future Generation Computer Systems, 128, 158–166.
Lucas, Y., Portier, P. E., Laporte, L., He-Guelton, L., Caelen, O., Granitzer, M., & Calabretto, S. (2020). Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs. Future Generation Computer Systems, 102, 393–402.
Nerurkar, P., Bhirud, S., Patel, D., Ludinard, R., Busnel, Y., & Kumari, S. (2021a). Supervised learning model for identifying illegal activities in Bitcoin. Applied Intelligence, 51, 3824–3843. https://doi.org/10.1007/s10489-020-02048-w
Nerurkar, P., Patel, D., Busnel, Y., Ludinard, R., Kumari, S., & Khan, M. K. (2021b). Dissecting Bitcoin blockchain: Empirical analysis of the Bitcoin network (2009–2020). Journal of Network and Computer Applications, 177, 102940. https://doi.org/10.1016/j.jnca.2020.102940
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., & Leiserson, C. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, 34, pp. 5363–5370). https://doi.org/10.1609/aaai.v34i04.5984
Patel, V., Pan, L., & Rajasegarar, S. (2020). Graph deep learning-based anomaly detection in Ethereum blockchain network. In Lecture Notes in Computer Science, 12496, pp. 132–148).
Santos, L. J. S., & Ocampo, S. R. (2018). Bayesian method with clustering algorithm for credit card transaction fraud detection. Romanian Statistical Review, 66, 103–120.
Song, A., Seo, E., & Kim, H. (2023). Anomaly VAE-Transformer: A deep learning approach for anomaly detection in decentralized finance. IEEE Access, 11, 1–15. https://doi.org/10.1109/ACCESS.2023.3313448
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958. https://doi.org/10.48550/arXiv.1207.0580
Sun, H., Li, J., & Zhu, X. (2023). Financial fraud detection based on the part-of-speech features of textual risk disclosures in financial reports. Procedia Computer Science, 221, 57–64. https://doi.org/10.1016/j.procs.2023.07.009
Tan, R., Tan, Q., Zhang, P., & Li, Z. (2021). Graph neural network for Ethereum fraud detection. In Proceedings of the IEEE International Conference on Big Knowledge (ICBK) (pp. 78–85).
Tian, H., Li, Y., Cai, Y., Shi, X., & Zheng, Z. (2021). Attention-based graph neural network for identifying illicit Bitcoin addresses. In Communications in Computer and Information Science, 1490, pp. 147–162). https://doi.org/10.1007/978-981-16-7993-3_11
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection: Machine learning methods. Proceedings of the International Symposium INFOTEH-JAHORINA, 18, 1–5. https://doi.org/10.1109/INFOTEH.2019.8717766
Vujicic, D., Jagodic, D., & Randic, S. (2018). Blockchain technology, Bitcoin, and Ethereum: A brief overview. Proceedings of the International Symposium INFOTEH-JAHORINA, 17, 1–6. https://doi.org/10.1109/INFOTEH.2018.8345547
Xiuguo, W., & Shengyong, D. (2022). An analysis on financial statement fraud detection for Chinese listed companies using deep learning. IEEE Access, 10, 22516–22532. https://doi.org/10.1109/ACCESS.2022.3153478
Yu, C., Zuo, Y., Feng, B., An, L., & Chen, B. (2019). An individual–group–merchant relation model for identifying fake online reviews: An empirical study on a Chinese e-commerce platform. Information Technology and Management, 20, 123–138. https://doi.org/10.1007/s10799-018-0288-1
Zheng, Y. (2022). GRU-GAT model for blockchain Bitcoin abnormal transaction detection. In Proceedings of the IEEE Conference on Telecommunications, Optics and Computer Science (TOCS) (pp. 666–674). https://doi.org/10.1109/TOCS56154.2022.10016137