网络钓鱼
数据库事务
计算机科学
图形
交易数据
数据挖掘
计算机安全
数据库
万维网
理论计算机科学
互联网
作者
Xuanchen Zhou,Wenzhong Yang,Xiaodan Tian
出处
期刊:Electronics
[MDPI AG]
日期:2023-02-16
卷期号:12 (4): 993-993
被引量:11
标识
DOI:10.3390/electronics12040993
摘要
In recent years, the losses caused by scams on Ethereum have reached a level that cannot be ignored. As one of the most rampant crimes, phishing scams have caused a huge economic loss to blockchain platforms and users. Under these circumstances, to address the threat to the financial security of blockchain, an Edge Aggregated Graph Attention Network (EGAT) based on the static subgraph representation of the transaction network is proposed. This study intends to detect Ethereum phishing accounts through the classification of transaction network subgraphs with the following procedures. Firstly, the accounts are used as nodes and the flow of transaction funds is used as directed edges to construct the transaction network graph. Secondly, the transaction record data of phishing accounts in the publicly available Ethereum are analyzed and statistical features of Value, Gas, and Timestamp values are manually constructed as node and edge features of the graph. Finally, the features are extracted and classified using the EGAT network. According to the experimental results, the Recall of the proposed method from the article is 99.3% on the dataset of phishing accounts. As demonstrated, the EGAT is more efficient and accurate compared with Graph2Vec and DeepWalk, and the graph structure features can express semantics better than manual features and simple transaction networks, which effectively improves the performance of phishing account detection.
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