Anomaly Detection Service for Blockchain Transactions Using Minimal Substitution-Based Label Propagation

Abstract

Supervising illicit activities on blockchain networks, such as money laundering, fraud, extortion, Ponzi schemes, and funding for terrorist organizations, presents significant challenges. Emerging machine learning methods for detecting abnormal transactions face hurdles due to high labeling costs, limited labeled data, and data imbalance. To address this, this article proposes a M inimal S ubstitution-based L abel P ropagation ( MSLP ) model to provide more labeled data to balance the graph data and complement the sample for anomalous transaction detection service in the blockchain networks. As far as we know, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more abnormal transactions with under-labeling budget constraints. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improved the F1 score of illegal transaction node detection by 2.6% to 8.2%.