计算机科学
块链
数据库事务
图形
同种类的
利用
理论计算机科学
事务处理
计算机安全
数据库
数学
组合数学
作者
Jaehyeon Kim,Sejong Lee,Yushin Kim,Se-young Ahn,Sunghyun Cho
出处
期刊:Sensors
[MDPI AG]
日期:2023-01-01
卷期号:23 (1): 463-463
被引量:10
摘要
Recently, cybercrimes that exploit the anonymity of blockchain are increasing. They steal blockchain users' assets, threaten the network's reliability, and destabilize the blockchain network. Therefore, it is necessary to detect blockchain cybercriminal accounts to protect users' assets and sustain the blockchain ecosystem. Many studies have been conducted to detect cybercriminal accounts in the blockchain network. They represented blockchain transaction records as homogeneous transaction graphs that have a multi-edge. They also adopted graph learning algorithms to analyze transaction graphs. However, most graph learning algorithms are not efficient in multi-edge graphs, and homogeneous graphs ignore the heterogeneity of the blockchain network. In this paper, we propose a novel heterogeneous graph structure called an account-transaction graph, ATGraph. ATGraph represents a multi-edge as single edges by considering transactions as nodes. It allows graph learning more efficiently by eliminating multi-edges. Moreover, we compare the performance of ATGraph with homogeneous transaction graphs in various graph learning algorithms. The experimental results demonstrate that the detection performance using ATGraph as input outperforms that using homogeneous graphs as the input by up to 0.2 AUROC.
科研通智能强力驱动
Strongly Powered by AbleSci AI