HNND: Hybrid Neural Network Detection for Blockchain Abnormal Transaction Behaviors

Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among nodes, which can capture key temporal information from node neighborhood transactions. Then, the Graph Convolutional Network (GCN) is adopted which captures abstract structural features of the transaction network. Further, the Stacked Denoising Autoencode (SDA) is developed to achieve adaptive fusion of thses features from different modules. Moreover, the SDA enhances robustness and generalization ability of node representation, leading to higher binary classification accuracy in detecting abnormal behaviors of blockchain accounts. Evaluations on a real-world abnormal transaction dataset demonstrate great advantages of the proposed HNND method over other compared methods.