Blockchain Abnormal Transaction Identification Method Based on Multi-Dimensional Feature Fusion and Edge Computing

With the widespread adoption of blockchain technology, various types of cybercrimes have emerged on the Ethereum network, including phishing scams, money laundering, and Ponzi schemes. These illegal activities not only threaten the security of the Ethereum ecosystem but also result in significant economic losses. Therefore, establishing efficient methods for anomalous transaction detection is crucial for blockchain security. However, existing approaches are limited by the use of single-type features and locally optimal feature selection, which restricts the comprehensiveness and performance of the models, making it difficult to effectively handle the diversity and heterogeneity of the data. To address this problem, this paper proposes a blockchain abnormal transaction identification method that combines multi-dimensional feature fusion, edge computing and future networks, which integrates three different types of features: statistical features, network structure features, and motif features. This method provides a comprehensive and multi-angle characterization of node features, fully exploiting the latent information in the data and enhancing the model’s performance and generalization ability. Experimental results demonstrate that the proposed method achieves excellent performance on the dataset, significantly improving the accuracy of anomalous transaction detection and showing its potential for enhancing blockchain transaction security, thereby providing new technical support for blockchain security. In addition, combining with edge computing and future network technologies, this method can also better cope with the complex environment in large-scale blockchain networks and provide new technical support for blockchain security.