Detection of Anomalous Bitcoin Transactions in Blockchain Using ML

Abstract

An Internet of Things (IoT)-enabled blockchain helps to ensure quick and efficient immutable transactions. Low-power IoT integration with the Bitcoin network has created new opportunities and difficulties for blockchain transactions. Utilising data gathered from IoT-enabled devices, this study investigates the application of ML regression models to analyse and forecast Bitcoin transaction patterns. Several ML regression algorithms, including Lasso Regression, Gradient Boosting, Extreme Boosting, Extra Tree, and Random Forest Regression, are employed to build predictive models. These models are trained using historical Bitcoin transaction data to capture intricate relationships between various transaction parameters. To ensure model robustness and generalisation, cross-validation techniques and hyperparameter tuning are also applied. The empirical results show that the Bitcoin cost prediction of blockchain transactions in terms of time series. Additionally, it highlights the possibility of fusing block- chain analytics with IoT data streams, illuminating how new technologies might work together to enhance financial institutions.