Interpretable Data Analytics in Blockchain Networks Using Variational Autoencoders and Model-Agnostic Explanation Techniques for Enhanced Anomaly Detection

Abstract

The rapid growth of blockchain technology has brought about increased transaction volumes and complexity, leading to challenges in detecting fraudulent activities and understanding data patterns. Traditional data analytics approaches often fall short in providing both accurate anomaly detection and interpretability, especially in decentralized environments. This paper explores the integration of Variational Autoencoders (VAEs), a deep learning-based anomaly detection technique, with model-agnostic explanation methods such as SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to enhance the interpretability of blockchain data analytics. Variational Autoencoders are leveraged to capture the underlying distribution of blockchain transactions, identifying anomalies by modeling deviations from learned patterns. To address the often-opaque nature of deep learning models, SHAP and LIME are employed to provide post-hoc explanations, offering insights into the key factors influencing the model’s predictions. This hybrid approach aims to not only detect irregularities in blockchain networks effectively but also to make the decision-making process transparent and understandable for stakeholders. By combining advanced anomaly detection with interpretable machine learning, this study presents a robust framework for improving the security and reliability of blockchain-based systems, providing a valuable tool for both developers and analysts in mitigating risks and enhancing trust in decentralized applications.