Graph Neural Network-Based Anomaly Detection in Blockchain Network

异常检测 计算机科学 块链 人工智能 数据库事务 图形 人工神经网络 图同构 机器学习 数据挖掘 计算机安全 理论计算机科学 折线图 程序设计语言
作者
Amit Sharma,Pradeep Kumar Singh,Elizaveta Podoplelova,Vadim Gavrilenko,Alexey Tselykh,Alexander Bozhenyuk
出处
期刊:Lecture notes in networks and systems 卷期号:: 909-925
标识
DOI:10.1007/978-981-99-1479-1_67
摘要

Long-term research has been done on anomaly identification. Its uses in the banking industry have made it easier to spot questionable hacker activity. However, it is more difficult to trick financial systems due to innovations in the financial sector like blockchain and artificial intelligence. Despite these technical developments, there have nevertheless been several instances of fraud. To address the anomaly detection issue, a variety of artificial intelligence algorithms have been put forth; while some findings seem to be remarkably encouraging, no clear winner has emerged. In order to identify fraudulent transactions, this article presented Inspection-L architecture based on graph neural network (GNN) with self-supervised deep graph infomax (DGI) and graph isomorphism network (GIN), with supervised knowledge methods, such as random forest (RF). The potential of self-supervised GNN in Bitcoin unlawful transaction detection has been demonstrated by the evaluation of the proposed technique on the Elliptic dataset. Results from experiments reveal that our approach outperforms existing standard methods for detecting anomalous events.
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