CoSemiGNN: Blockchain fraud detection with dynamic graph neural networks based on co-association of semi-supervised

With the development of blockchain technology, the increasing number of cyber frauds has caused huge economic losses, prompting more and more researchers to focus on how to effectively detect criminal activities in the blockchain transaction environment. Currently, graph neural network (GNN)-based methods have made significant progress in the field of blockchain illegal transaction detection due to their advantages in extracting graph structure features. However, existing illegal transaction pattern detection methods usually rely on historical labeled data. In the blockchain transaction environment, transaction data changes over time, and it is often difficult to obtain transaction labels. As a result, the performance of these methods is often unsatisfactory when faced with newly distributed transaction data. To address this challenge, this paper proposes a dynamic graph neural network based on co-association of semi-supervised (CoSemiGNN) for more efficiently identifying illegal transactions in blockchain environments under conditions of dynamically changing transaction data. The model combines semi-supervised learning with a dynamic graph neural network, enabling it to effectively identify novel illegal transaction patterns from unlabeled data and adapt to the evolving blockchain network environment. Specifically, CoSemiGNN captures features of novel transactions by integrating semi supervised learning results. It utilizes co-occurrence relations of edges and co-occurrence feature aggregation of nodes to skillfully integrate semi-supervised methods into feature extraction of transaction graphs, enabling the model to extract novel illegal transaction patterns from unlabeled data. In addition, the model utilizes self attention recurrent neural networks (RNNs) to capture temporal information in transactions, ensuring the dynamics of CoSemiGNN. Finally, we theoretically analyze the model, and experiments on a real Bitcoin transaction dataset demonstrate that CoSemiGNN outperforms existing methods by as much as 30 % in terms of F1 scores for detecting illegal transactions when the transaction data undergoes distributional migration. This research compensates the problem that existing methods ignore the distributional changes of blockchain transaction data, and provides a new perspective and an effective solution for blockchain illegal transaction detection.