A Comparative Analysis for Anomaly Detection in Blockchain Networks Using Machine Learning Techniques

Abstract

Cryptocurrency-related crimes peaked in 2021, and current trends indicate the enduring presence of blockchain technology. With transaction volumes projected to rise, manual investigations into criminal activities will soon become impractical. Consequently, anomaly detection systems are essential for upholding the financial integrity of blockchain networks. Despite the security measures implemented through consensus mechanisms, blockchain platforms remain vulnerable to illicit behavior. Leveraging machine learning presents a promising strategy for mitigating these risks. This study undertakes a thorough comparison of various machine-learning algorithms aimed at detecting suspicious transactions within blockchain networks. The evaluated techniques encompass Support Vector Machine, Decision Tree, Logistic Regression, K-means, Random Forest, XGBoost, Local Outlier Factor, Light Gradient, and Isolation Forest.