The rapid evolution of cryptocurrency markets, coupled with the escalating sophistication of fraudulent activities, has amplified the necessity for advanced machine learning (ML) methodologies to augment the detection, prediction, and behavioral analysis of Bitcoin transactions. Conventional approaches to fraud detection and market analysis frequently falter in capturing cryptocurrency ecosystems' intricate, dynamic, and exceedingly volatile essence. This research elucidates a data-driven framework that employs machine learning to identify scams, forecast Bitcoin market fluctuations, and scrutinize user behavior patterns within the U.S. cryptocurrency domain. By leveraging extensive Bitcoin transaction datasets enriched with features such as transaction volumes, timestamps, wallet activities, and anomaly indicators, the study deploys a diverse array of models: Random Forest, XGBoost, Logistic Regression, Support Vector Machines (SVMs), Graph Neural Networks (GNNs), Isolation Forest, and Autoencoders for fraud detection; Long Short-Term Memory (LSTM) networks and Deep Q-Learning for price prediction and trend forecasting; and K-Means clustering for the behavioral analysis of user activities. The study integrates time-series analysis, anomaly detection pipelines, and dimensionality reduction techniques to enhance predictive robustness and address challenges such as pronounced volatility, concept drift, and data sparsity. Moreover, the data imbalance issues intrinsic to fraud detection are confronted through strategic resampling methodologies. Model performance is meticulously assessed utilizing metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and RMSE for forecasting endeavors.
Saru Kumari (2025) studied this question.