Graph Neural Network-Based Anomaly Detection in Blockchain Network

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.