Zobrazit v EDS

Ethereum phishing detection based on graph neural networks.

Uloženo v:
Podrobná bibliografie
Název: Ethereum phishing detection based on graph neural networks.
Autoři: Xiong, Ao, Tong, Yuanzheng, Jiang, Chengling, Guo, Shaoyong, Shao, Sujie, Huang, Jing, Wang, Wei, Qi, Baozhen
Zdroj: IET Blockchain; Sep2024, Vol. 4 Issue 3, p226-234, 9p
Abstrakt: With the development of blockchain, cryptocurrencies are also showing a boom. However, due to the decentralized and anonymous nature of blockchain, cryptocurrencies have inevitably become a hotbed for fraudulent crimes. For example, phishing scams are frequent, which not only jeopardize the financial security of blockchain, but also hinder the promotion of blockchain technology. To solve this problem, this paper proposes a graph neural network‐based phishing detection method for Ethereum, and validates it using Ethereum datasets. Specifically, this paper proposes a feature learning algorithm named TransWalk, which consists of a random walk strategy for transaction networks and a multi‐scale feature extraction method for Ethereum. Then, an Ethereum phishing fraud detection framework is built based on TransWalk, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection. [ABSTRACT FROM AUTHOR]
Copyright of IET Blockchain is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Buďte první, kdo okomentuje tento záznam!
Nejprve se musíte přihlásit.