Identifying Illicit Activities in Blockchain Transaction Graph Networks

In recent years, blockchain technology has gained widespread attention for its immutable and distributed ledger mechanism that ensures security and transparency among all participants. However, the decentralized nature of the blockchain network consequently presents its unique challenges in detecting fraudulent activities that may be executed by malicious actors. The traditional detection methods, such as rule-based systems, may not be sufficient to capture the complex and evolving nature of these activities. This paper explores the application of machine learning and transaction graph representation to detect suspicious accounts on the World Asset Exchange (WAX) blockchain. By leveraging dynamic subgraph embedding generation and contrastive representation learning, the proposed approach primarily targets the identification of suspicious transaction behaviors indicative of fraudulent activity. The contrastive representation learning approach enhances the generation of subgraph embeddings through a contrastive loss function to effectively discriminate between potentially fraudulent and legitimate transaction behavior by optimizing the distances in the embedding space. This process significantly enhances the classification accuracy, particularly for the imbalanced minority class often seen in fraud detection scenarios. The results of the experimental validations indicate the presence of potentially fraudulent activities and highlight the effectiveness of the anomaly ranking mechanism in identifying new, previously unseen cases.