Enhancing Financial Fraud Detection in Bitcoin Networks Using Ensemble Deep Learning

Abstract

This paper introduces an innovative approach to bolster financial fraud detection within the Bitcoin network using ensemble deep learning models. By synergistically merging ensemble techniques with state-of-the-art deep learning methodologies, this study creates a robust framework that enhances security and trust in financial systems. Through meticulous data preprocessing and feature engineering, the proposed ensemble model, comprising Multi-Layer Perceptron (MLP), Feedforward Neural Network (FNN), and Attention LSTM, undergoes comprehensive training and evaluation. Results underscore the ensemble's remarkable performance, surpassing individual models in accuracy, precision, and recall. Notably, the ensemble achieves an accuracy of 99.62%, along with exceptional precision and recall values exceeding 99%. These outcomes validate the ensemble's capacity to detect both fraudulent and legitimate transactions with unprecedented accuracy. By effectively combining advanced machine learning techniques with the intricacies of blockchain-based transactions, this research contributes to building a more secure and reliable financial ecosystem. The findings open avenues for future research, emphasizing the potential of ensemble deep learning models to fortify defenses against evolving financial fraud strategies, fostering trust and integrity in digital transactions.