BlockDetective: A GCN-based student–teacher framework for blockchain anomaly detection

Abstract

The anonymous and tamper-proof nature of the blockchain poses significant challenges in auditing and regulating the behaviour and data on the chain. Criminal activities and anomalies are frequently changing, and fraudsters are devising new ways to evade detection. Moreover, the high volume and complexity of transactions and asymmetric errors make data classification more challenging. Also, class imbalances and high labelling costs are hindering the development of effective algorithms. In response to these issues, the authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre-training and fine-tuning, allowing the pre-trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light-weight model (student) that provides abstract and top-level information. The authors’ experimental results show that BlockDetective outperforms state-of-the-art research methods by achieving top-notch performance in detecting fraudulent transactions on the blockchain. This framework can assist regulators and auditors in detecting and preventing fraudulent activities on the blockchain, thereby promoting a more secure and transparent financial system.