Bitcoin anomaly: adaptive anomaly detection with automated signing of blockchain-based bitcoin system using weighted recurrent neural network attention mechanism

Abstract

In this paper, we developed anomaly detection based on machine learning-based with the automated signing of the blockchain transaction system to effectively detect the anomalies to prevent the leakage of information from the bitcoin system. Initially, the anomalies data is collected from online resources. The automated signing of the transaction system is performed using machine learning. A blockchain transaction is used for the personalised identification of anomalies transactions. It secures the transactions from fraudulent blockchain transactions. Then, the anomaly detection is done by an optimised recurrent neural network with attention mechanism (ORNN-AM). Here, the parameters are optimised using fitness of firefly and driving training-based optimisation (FFDTO). Anomaly detection with the automated signing of blockchain transactions using machine learning techniques helps to detect anomalies effectively. The performance of anomaly detection with the automated signing of the blockchain transactions system is compared to other conventional anomaly detection models.