CrossAAD: Cross-Chain Abnormal Account Detection

Cross-chain technology enhances the interconnection among independent blockchains and mitigates the isolated data island. It achieves the asset transfer/exchange between different blockchains via cross-chain transactions. The lack of uniformity in cross-chain architecture increases the difficulty of cross-chain transaction regulation. Abnormal account detection can effectively identify malicious behaviors. However, existing schemes are only designed for the single blockchain and cannot directly be applied to cross-chain due to independent transaction structures. It still lacks feasible abnormal account detection mechanism to supervise cross-chain transactions. In this paper, we propose CrossAAD, a cross-chain abnormal account detection approach to effectively protect cross-chain transactions. CrossAAD is built on top of a new cross-chain bridge dataset, integrated with the intensive feature extraction & processing and the adjusted XGBoost model. Four typical models are compared to analyze their applicability in cross-chain scenarios. We implement a prototype system of CrossAAD based on a real dataset with 425,889 transactions. The experimental results show that CrossAAD has a comparable performance with state-of-the-art single-chain schemes, with 95% precision and 87% recall on normal labels, and 71% precision and 87% recall on abnormal labels.