CNN Based Deep Learning Modeling with Explainability Analysis for Detecting Fraudulent Blockchain Transactions

In the era of growing cryptocurrency adoption, Blockchain has emerged as a leading player in the digital payment landscape. However, this widespread popularity also brings forth various security challenges, including the need to safeguard against fraudulent activities. One of the paramount challenges in this regard is the detection of fraudulent transactions within the realm of Bitcoin data. This task significantly influences the trust and security of digital payments. Yet, it’s a formidable challenge given the relatively low occurrence of fraudulent Bitcoin transactions. While deep learning techniques have demonstrated their prowess in fraud detection, there remains a scarcity of studies exploring their potential, particularly in Blockchain. This study aims to fill that gap, focusing on our 1D Convolutional Neural Network (CNN) model, which combines the power of eXplainable Artificial Intelligence (XAI) techniques. To understand how our model works and explain its decisions, we use the Shapley Additive exPlanation (SHAP) method, which measures each feature’s impact. We also deal with data imbalance by exploring various methods to balance fraudulent and benign Bitcoin transaction data. Our findings are significant, indicating that the proposed 1D CNN model achieves higher accuracy while simultaneously reducing the False Positive Rate (FPR).