Ethereum Phishing Scam Detection based on Graph Embedding and Semi-Supervised Learning


KIPS Transactions on Computer and Communication Systems, Vol. 12, No. 5, pp. 165-170, May. 2023
https://doi.org/10.3745/KTCCS.2023.12.5.165,   PDF Download:
Keywords: Blockchain, Ethereum, Phishing Scam, Graph Embedding, Semi-supervised learning
Abstract

With the recent rise of blockchain technology, cryptocurrency platforms using it are increasing, and currency transactions are being actively conducted. However, crimes that abuse the characteristics of cryptocurrency are also increasing, which is a problem. In particular, phishing scams account for more than a majority of Ethereum cybercrime and are considered a major security threat. Therefore, effective phishing scams detection methods are urgently needed. However, it is difficult to provide sufficient data for supervised learning due to the problem of data imbalance caused by the lack of phishing addresses labeled in the Ethereum participating account address. To address this, this paper proposes a phishing scams detection method that uses both Trans2vec, an effective graph embedding techique considering Ethereum transaction networks, and semi-supervised learning model Tri-training to make the most of not only labeled data but also unlabeled data.


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Cite this article
[IEEE Style]
Y. Cheong, G. Kim, D. Im, "Ethereum Phishing Scam Detection based on Graph Embedding and Semi-Supervised Learning," KIPS Transactions on Computer and Communication Systems, vol. 12, no. 5, pp. 165-170, 2023. DOI: https://doi.org/10.3745/KTCCS.2023.12.5.165.

[ACM Style]
Yoo-Young Cheong, Gyoung-Tae Kim, and Dong-Hyuk Im. 2023. Ethereum Phishing Scam Detection based on Graph Embedding and Semi-Supervised Learning. KIPS Transactions on Computer and Communication Systems, 12, 5, (2023), 165-170. DOI: https://doi.org/10.3745/KTCCS.2023.12.5.165.