Abstract
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security. Most existing Ponzi scheme detection techniques suffer from two major restricted problems: the lack of motivation for temporal early warning and failure to fuse multi-source information finally cause the lagging and unsatisfactory performance of Ethereum Ponzi scheme detection. In this paper, we propose a dual-channel early warning framework for Ethereum Ponzi schemes, named Ponzi-Warning, which performs feature extraction and fusion on both code and transaction levels. Moreover, we represent a temporal evolution augmentation strategy for generating transaction graph sequences, which can effectively increase the data scale and introduce temporal information. Comprehensive experiments on our Ponzi scheme datasets demonstrate the effectiveness and timeliness of our framework for detecting the Ponzi contract accounts.
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References
Frankel, T.: The Ponzi Scheme Puzzle: A History and Analysis of Con Artists and Victims. Oxford University Press, London (2012)
Springer, M.: The Politics of Ponzi Schemes: History, Theory, and Policy. Routledge, Milton Park (2020)
Morris, D.Z.: The rise of cryptocurrency ponzi schemes. The Atlantic, vol. 31 (2017)
Bartoletti, M., Carta, S., Cimoli, T., Saia, R.: Dissecting ponzi schemes on ethereum: identification, analysis, and impact. Futur. Gener. Comput. Syst. 102, 259–277 (2020)
Lou, Y., Zhang, Y., Chen, S.: Ponzi contracts detection based on improved convolutional neural network. In: 2020 IEEE International Conference on Services Computing (SCC), pp. 353–360. IEEE (2020)
Jung, E., Le Tilly, M., Gehani, A., Ge, Y.: Data mining-based ethereum fraud detection. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp. 266–273. IEEE (2019)
Zhou, J., Hu, C., Chi, J., Wu, J., Shen, M., Xuan, Q.: Behavior-aware account de-anonymization on ethereum interaction graph. arXiv preprint. arXiv:2203.09360 (2022)
Jin, C., Jin, J., Zhou, J., Wu, J., Xuan, Q.: Heterogeneous feature augmentation for ponzi detection in ethereum. arXiv preprint. arXiv:2204.08916 (2022)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint. arXiv:1609.02907 (2016)
Hu, T., et al.: Transaction-based classification and detection approach for Ethereum smart contract. Inf. Process. Manag. 58(2), 102462 (2021)
Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., Zhou, Y.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 World Wide Web Conference, pp. 1409–1418 (2018)
Chen, W., Zheng, Z., Ngai, E.C.H., Zheng, P., Zhou, Y.: Exploiting blockchain data to detect smart ponzi schemes on ethereum. IEEE Access 7, 37575–37586 (2019)
Zhang, Y., Yu, W., Li, Z., Raza, S., Cao, H.: Detecting ethereum ponzi schemes based on improved lightgbm algorithm. IEEE Trans. Comput. Soc. Syst. 9(2), 624–637 (2021)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint. arXiv:1710.10903 (2017)
Ranjan, E., Sanyal, S., Talukdar, P.: Asap: adaptive structure aware pooling for learning hierarchical graph representations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5470–5477 (2020)
Lee, J., Lee, I., Kang, J.: Self-attention graph pooling. In: International Conference on Machine Learning, pp. 3734–3743. PMLR (2019)
Knyazev, B., Taylor, G.W., Amer, M.: Understanding attention and generalization in graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint. arXiv:1511.05493 (2015)
Acknowledgments
This work was partially supported by the National Key R &D Program of China under Grant 2020YFB1006104, by the Key R &D Programs of Zhejiang under Grants 2022C01018 and 2021C01117, by the National Natural Science Foundation of China under Grant 61973273 and 62103374, and by the Zhejiang Provincial Natural Science Foundation of China under Grant LR19F030001, and by Basic Public Welfare Research Project of Zhejiang Province Grant LGF20F020016 and Open Project of the Key Laboratory of Public Security Informatization Application Based on Big Data Architecture Grant 2020DSJSYS003.
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Jin, J., Zhou, J., Jin, C., Yu, S., Zheng, Z., Xuan, Q. (2022). Dual-Channel Early Warning Framework for Ethereum Ponzi Schemes. In: Meng, X., Xuan, Q., Yang, Y., Yue, Y., Zhang, ZK. (eds) Big Data and Social Computing. BDSC 2022. Communications in Computer and Information Science, vol 1640. Springer, Singapore. https://doi.org/10.1007/978-981-19-7532-5_17
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DOI: https://doi.org/10.1007/978-981-19-7532-5_17
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