Cross-chain Anomalous Transaction Detection Based on BERT Model

Cross-chain is an important technology that breaks the ″information silos″ of blockchain networks and facilitates interoperability between different blockchain networks. Cross-chain bridges have become an important technique for asset and information transfer between heterogeneous blockchains. In recent years, attacks against cross-chain bridge vulnerabilities have occurred frequently, and the cross-chain transaction anomalies caused by these attacks have resulted in economic losses of up to billions. However, research on the problem of anomalous transactions in cross-chain bridges is lacking and detection efforts are highly dependent on manually summarized anomalous patterns of transaction sequences. In this study, a cross-chain anomalous transaction detection method based on the Bidirectional Encoder Representations from Transformer (BERT) model is proposed, which overcomes the limitations of existing detection methods that rely on manual experience by providing two detection modes based on feature extraction. The first mode aims to extract features more accurately by automatically extracting cross-chain transaction sequences with key features from cross-chain native transaction data based on the transaction status and then fine-tuning the BERT-Base-Uncased pretrained model to adapt to the anomalous transaction detection task using cross-chain transaction sequence text data. The second mode aims to compensate for the possible feature inadequacies that may occur by considering only key cross-chain transaction sequences and to solve the anomaly detection task by directly fine-tuning the BERT-Base-Uncased pretrained model using the original transaction text data with comprehensive features. The experiments use real cross-chain data from existing studies to evaluate the proposed detection methods. The results show that both detection modes can effectively detect anomalous cross-chain transactions, that is, the precision rate, recall rate, and F1 value reach 100%.