Abstract
With the rapid growth of Blockchain, Bitcoin, a key Blockchain application, has received a lot of attention. Bitcoin trading has made transactions more convenient. However, because of the anonymity, complexity, and lack of third parties, criminal activity against Blockchain Bitcoin applications is frequent. Individuals and society have suffered enormous losses as a result of money laundering, fraud, airdrop, ransom, and other peculiar abnormal transactions. We propose the GRU-GAT based model to detect abnormal transactions in Blockchain and reduce the loss and harm caused by abnormal transactions. Our proposed model employs a bidirectional recurrent neural network to extract the features of Blockchain Bitcoin transactions, graph attention networks for feature weighting, and the spatio-temporal aspects of transactions to integrate the features of Bitcoin transactions for anomaly identification. We conduct experiment through the publicly available elliptic dataset. The results reveal that the suggested method outperforms the comparison model in terms of accuracy and enhances the accuracy of detecting anomalous transactions in Blockchain digital currency.
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