Abstract
Blockchain has become a hot topic in current academic research due to its decentralization, imtamability and anonymity. However it also makes blockchain a tool for money laundering, fraud, extortion and other illegal activities. Therefore, it is particularly important to supervise and detect abnormal transactions on blockchain. In our paper, graph data structure is used to express blockchain node transactions. Aiming at the problem of too few abnormal transaction samples of block chain, k-rate sampling and feature similarity are used to solve the problem of unbalanced data. Further more, we select some features for feature preprocessing according to the distribution of multiple features of blockchain transactions. Finally, the blockchain transaction topology is used for multi-graph convolutional neural network machine learning methods to train the blockchain transaction data. So the abnormal nodes and non-abnormal nodes classification model is obtained. And the abnormal transactions of blockchain is detected by using the classification model. Experimental results show that compared with traditional algorithms such as logistic regression (LR), multi-layer perceptron (MLP) and linear regression (LR), the anomaly detection algorithm based on sample equalization and feature engineering has significantly improved recall rate. This provides a theoretical and practical basis for our next work – tracing abnormal nodes of blockchain, which has a good prospect for industrial application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941 (2016)
Chen, W., Zheng, Z., Cui, J., et al.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 World Wide Web Conference. pp. 1409–1418 (2018)
Weber, M., Domeniconi, G., Chen, J., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)
Pareja, A., Domeniconi, G., Chen, J., et al.: Evolvegcn: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(04), pp. 5363–5370 (2020)
Veličković, P., Cucurull, G., Casanova, A., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Du, J., Zhang, S., Wu, G., et al.: Topology adaptive graph convolutional networks. arXiv preprint arXiv:1710.10370 (2017)
Brockschmidt, M.: GNN-film: graph neural networks with feature-wise linear modulation. In: International Conference on Machine Learning. PMLR, pp. 1144–1152 (2020)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Morris, C., Ritzert, M., Fey,. M, et al.: Weisfeiler and leman go neural: higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33(01), pp. 4602–4609 (2019)
Wu, F., Souza, A., Zhang, T., et al.: Simplifying graph convolutional networks. In: International Conference on Machine Learning. PMLR, pp. 6861–6871 (2019)
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(04), 5470–5477 (2020)
Bianchi, F.M., Grattarola, D., Livi, L., et al.: Graph neural networks with convolutional arma filters. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2021)
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., et al.: Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292 (2015)
Chiang, W.L., Liu, X., Si, S., et al.: Cluster-GCN: an efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257–266 (2019)
Xu, Q. Song, Z., Goh, R.S.M., Li, Y.: Building an ethereum and IPFS-based decentralized social network system. In: 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS) (2018)
Kim, D., Oh, A.: How to find your friendly neighborhood: Graph attention design with self-supervision. In: International Conference on Learning Representations (2020)
Acknowledgement
This work is supported in part by the National Key R &D Program of China (No. 2020YFB1006002), in part by the National Natural Science Foundation of China under Grant 62071092, and in part by Key Lab of Information Network Security, Ministry of Public Security under Grant C19603.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Han, H., Wang, R., Chen, Y., Xie, K., Zhang, K. (2022). Research on Abnormal Transaction Detection Method for Blockchain. In: Svetinovic, D., Zhang, Y., Luo, X., Huang, X., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2022. Communications in Computer and Information Science, vol 1679. Springer, Singapore. https://doi.org/10.1007/978-981-19-8043-5_16
Download citation
DOI: https://doi.org/10.1007/978-981-19-8043-5_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8042-8
Online ISBN: 978-981-19-8043-5
eBook Packages: Computer ScienceComputer Science (R0)