Abstract
Ethereum, as the second generation of blockchain technology, it not only brings many advantages, but also spawns various malicious incidents. Ethereum’s anonymity makes it a hotbed of cybercrime, causing huge losses to users and severely disrupting the Ethereum ecosystem. To this end, this paper proposes a method for detecting malicious accounts in Ethereum based on ETH tracking tree (ETH-TT). Firstly, based on the transaction history replay mechanism, an ETH tracking algorithm for tracking the transaction amount of Ethereum is designed to obtain the ETH tracking tree, and extract sequence features from it. Then train the LSTM model to reduce the dimension of the sequence features to obtain the output features. Finally, detection is done by a machine learning classifier, fused with manual features from account transaction history. We uses 5576 malicious accounts and 4968 normal accounts as dataset for experiments. The results show that the ETH-TT method can achieve an F1-score of 95.4% with the cooperation of the XGBoost classifier, which is better than the detection method using only manual features.
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Notes
- 1.
Multi-signature wallet version 1.5 of Parity Technologies.
References
Chen, H., Pendleton, M., Njilla, L., Xu, S.: A survey on Ethereum systems security: vulnerabilities, attacks, and defenses. ACM Comput. Surv. (CSUR) 53(3), 1–43 (2020)
Cryptoscamdb (2019). https://api.cryptoscamdb.org
Farrugia, S., Ellul, J., Azzopardi, G.: Detection of illicit accounts over the Ethereum blockchain. Expert Syst. Appl. 150, 113318 (2020)
Graves, A.: Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
IRMkz: Ethereum addresses-darklist. https://github.com/MyEtherWallet/ethereum-lists/blob/master/src/addresses/addresses-darklist.json (2020)
Jentzsch, C.: A standard decentralized autonomous organization (DAO) framework written in solidity to run on the Ethereum blockchain (2016). https://github.com/slockit/DAO/tree/v1.0
Kumar, N., Singh, A., Handa, A., Shukla, S.K.: Detecting malicious accounts on the Ethereum blockchain with supervised learning. In: Dolev, S., Kolesnikov, V., Lodha, S., Weiss, G. (eds.) CSCML 2020. LNCS, vol. 12161, pp. 94–109. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49785-9_7
Lee, X.T., Khan, A., Sen Gupta, S., Ong, Y.H., Liu, X.: Measurements, analyses, and insights on the entire Ethereum blockchain network. In: Proceedings of The Web Conference 2020, pp. 155–166 (2020)
Medvedev, E.: blockchain-etl/ethereum-etl (2020). https://github.com/blockchain-etl/ethereum-etl
Poursafaei, F., Hamad, G.B., Zilic, Z.: Detecting malicious Ethereum entities via application of machine learning classification. In: 2020 2nd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), pp. 120–127. IEEE (2020)
Tan, M.: Ethereum blockchain explore (2015)r. https://etherscan.io
Tan, M.: Heist accounts (2015). https://etherscan.io/accounts/label/heist
Tan, M.: Upbit hack (2015). https://etherscan.io/accounts/label/upbit-hack
Wood, G., et al.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151(2014), 1–32 (2014)
Wu, J., et al.: Who are the phishers? Phishing scam detection on Ethereum via network embedding. arxiv prepr. arxiv:1911.09259 (2019)
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Xu, J.J.: Are blockchains immune to all malicious attacks? Financ. Innov. 2(1), 1–9 (2016)
Acknowledgments
This work was supported by the National Key Research and Development Program of China under grant No. 2019YFC1521101.
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Yuan, Z., Yang, T., Cao, J. (2022). ETH-TT: A Novel Approach for Detecting Ethereum Malicious Accounts. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_7
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