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Temporal high-order proximity aware behavior analysis on Ethereum

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Abstract

Ethereum, the most popular public blockchain with the capability of smart contracts and the cryptocurrency Ether, is escalating in the number of account addresses and transactions since its birth. Due to the decentralisation of the Ethereum blockchain and the anonymity of its users, Ethereum serves as a noteworthy environment for malicious activities that are difficult to unearth. As a result, understanding the behaviors of the account addresses on Ethereum has become an imperative problem receiving much attention very recently. Existing works for such task mainly rely on extracting statistical features of account addresses and applying machine learning techniques to group or identify them. However, seldom prevailing approaches take temporal information and high-order interactions among the account addresses into consideration. To this end, we propose a novel approach coined THCD (T emporal H igh-order proximity aware C ommunity D etection) for behavior analysis on Ethereum from the perspective of graph mining. First, frequent temporal motifs are mined over a transaction graph constructed by the Ethereum block transactions. Next, we define the high-order proximity between two accounts based on these temporal motif occurrences. Finally, a novel temporal motif-aware community detection method is devised to find account communities over the defined high-order proximity. Experiments on four real datasets constructed from Ethereum blocks demonstrate the effectiveness of our approach. Some discovered suspicious accounts are confirmed by real-world reports. Meanwhile, THCD is scalable to large-scale transaction datasets.

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Notes

  1. https://github.com/ethereum/wiki/wiki/White-Paper

  2. https://www.carbonblack.com/2018/06/07/carbon-black-threat-report-cryp_tocurrency-gold-rush-dark-web/

  3. A mixer is an online software service that can swap cryptocurrencies for ones with different transaction histories.

  4. https://medium.com/crypto-integrity/fake-volumes-in-cryptocurrency-markets-february-report-fec9329f1f98

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Acknowledgements

The research work is supported by the National Key Research and Development Program of China under Grant No.2017YFB1002104, the National Natural Science Foundation of China under Grant No. 92046003, 61976204, U1811461, 61672499, Key Special Project of Beijing Municipal Science & Technology Commission (Z181100003218018). Xiang Ao is also supported by the Project of Youth Innovation Promotion Association CAS and Beijing Nova Program Z201100006820062. We thank Haibo Zhou for his valuable suggestions for this work.

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Authors and Affiliations

  1. Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China

    Xiang Ao, Yang Liu, Zidi Qin & Qing He

  2. University of Chinese Academy of Sciences, Beijing, China

    Xiang Ao, Yang Liu, Zidi Qin, Yi Sun & Qing He

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Yi Sun

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  1. Xiang Ao
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Correspondence to Xiang Ao.

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This article belongs to the Topical Collection: Special Issue on Emerging Blockchain Applications and Technology

Guest Editors: Rui Zhang, C. Mohan, and Ermyas Abebe

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Ao, X., Liu, Y., Qin, Z. et al. Temporal high-order proximity aware behavior analysis on Ethereum. World Wide Web 24, 1565–1585 (2021). https://doi.org/10.1007/s11280-021-00875-6

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