Deep Learning-Based Fraud Detection in Cryptocurrency Transactions Using Convolutional Neural Networks

Abstract

The exponential growth of cryptocurrency transactions has simultaneously increased the complexity and frequency of fraudulent activities. This research presents a novel approach to cryptocurrency transaction fraud detection utilizing Convolutional Neural Networks (CNN), a state-of-the-art deep learning technique. The study leverages a comprehensive dataset of cryptocurrency transactions, employing advanced feature engineering and preprocessing techniques to enhance model performance. Our proposed CNN model demonstrates significant potential in identifying fraudulent transactions with high accuracy and reliability. Key findings reveal the model achieved significant training and validation accuracy, indicating robust generalization capabilities. The performance metrics were validated through detailed loss curve analysis, which demonstrated minimal overfitting and effective learning dynamics. The proposed methodology contributes to the emerging field of blockchain security by offering a sophisticated machine learning framework for real-time fraud detection. Experimental results highlight the CNN model's effectiveness in distinguishing between legitimate and fraudulent cryptocurrency transactions, presenting a promising solution for financial institutions and cryptocurrency platforms.