A hybrid deep learning-based framework for enhanced real-time fraud detection in Bitcoin transactions

Abstract

Fraud risks are on the rise with the increase in cryptocurrency transactions; the traditional detection methods become inadequate. This paper proposes a hybrid deep-learning framework for real-time fraud detection in Bitcoin transactions. Algorithms like Random Forest, support vector machine (SVM), Logistic Regression, and XGBoost are used to analyse transaction patterns and anomalies with a high level of accuracy. Different models have been tested in transaction data for Bitcoin, and the best-performing model was XGBoost with an accuracy of 96.94%. Advanced machine learning techniques enrich a system through secure data-driven insights and real-time anomaly detection, thus enhancing fraud risk detection. The obstacles faced are scalability, privacy issues, and inability to adjust models according to the evolving fraud technique. Future advancements might deal with federated learning, encryption methods, and cross-platform prevention to make the detection of fraud more secure. This paper indicates how well deep learning-based detection of fraud can scale and work efficiently to strengthen trust in digital financial systems.