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A Survey on Ethereum Illicit Detection

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13340))

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Abstract

In recent years, blockchain technology has developed rapidly and has been widely used in finance, healthcare, and energy. As the 2.0 version of the blockchain, Ethereum has been seen as the mainstream smart contract development and operation platform, which attracted the attention of criminals. Many Ethereum financial crimes have occurred from time to time, making the Ethereum trading environment facing serious security problems. The safety supervision of the blockchain cannot be delayed. Among them, the detection and early warning of illicit transactions has become the top priority. Traditional machine learning, graph embedding, deep learning and other machine learning methods have all been used for illicit detection. The paper introduces a comprehensive investigation of illicit detection on Ethereum using machine learning technology, it has two sides: one is from the perspective of Ethereum transaction data, using general detection methods; the other is for specific types of illicit transactions detection (Including Ponzi schemes and honeypot contracts). For each transaction type, summarized relevant research ideas, model establishment and evaluation effects. Finally, the paper analyzes the general trend of the current Ethereum illicit detection research, and looks forward to the future research directions and challenges.

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Acknowledgement

The work is supported by 2020 Scientific Research Project of Jiangsu Police Academy: Blockchain supervision technology research (2020SJYZR02) and 2021 General Project of Philosophy and Social Science Research in Jiangsu Universities: Research on the Construction of Social Credit System Based on Blockchain (2021SJA0497).

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Authors and Affiliations

  1. Basic Course Teaching and Research Department, Jiangsu Police Institute, Nanjing, 210031, People’s Republic of China

    Meng Li

  2. Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center, Nanjing, 210031, People’s Republic of China

    Meng Li

  3. Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department, Nanjing, 210031, People’s Republic of China

    Meng Li

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  1. Meng Li
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Correspondence to Meng Li .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Li, M. (2022). A Survey on Ethereum Illicit Detection. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_18

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  • DOI: https://doi.org/10.1007/978-3-031-06791-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06790-7

  • Online ISBN: 978-3-031-06791-4

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