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Phishing Scam Detection for Ethereum Based on Community Enhanced Graph Convolutional Networks

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1965))

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Abstract

Blockchain technology has garnered a lot of interest recently, but it has also become a breeding ground for various network crimes. Cryptocurrency, for example, has suffered losses due to network phishing scams, posing a serious threat to the security of blockchain ecosystem transactions. To create a favorable investment environment, we propose a community-enhanced phishing scam detection model in this paper. We approach network phishing detection as a graph classification task and introduce a network phishing detection graph neural network framework. Firstly, we construct an Ethereum transaction network and extract transaction subgraphs, and corresponding content features from it. Based on this, we propose a community-enhanced graph convolutional network (GCN)-based detection model. It extracts more reasonable node representations in the GCN neighborhoods and explores the advanced semantics of the graph by defining community structure and measuring the similarity of nodes in the community. This distinguishes normal accounts from phishing accounts. Experiments on different large-scale real-data sets of Ethereum consistently demonstrate that our proposed model performs better than related methods.

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Notes

  1. 1.

    https://blog.chainalysis.com/the-rise-of-cybercrime-on-ethereum/.

  2. 2.

    https://theripplecryptocurrency.com/bee-token-scam/.

  3. 3.

    https://etherscan.io/charts.

  4. 4.

    https://etherscamdb.info/scams.

  5. 5.

    https://etherscan.io/.

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Acknowledgements

This research was supported by the National Key R &D Program of China No. 2022YFB2702504. It is also supported by the Fundamental Research Funds for the Central Universities (226-2022-00064).

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Authors and Affiliations

  1. School of Software Technology, Zhejiang University, Ningbo, China

    Keting Yin & Binglong Ye

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  1. Keting Yin
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  2. Binglong Ye
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Correspondence to Keting Yin .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Yin, K., Ye, B. (2024). Phishing Scam Detection for Ethereum Based on Community Enhanced Graph Convolutional Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_16

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  • DOI: https://doi.org/10.1007/978-981-99-8145-8_16

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