PonziFinder: Attention-Based Edge-Enhanced Ponzi Contract Detection

Abstract

Ponzi contracts are fraudulent investment scams that promise high returns with little risk to investors. However, existing methods for detecting Ponzi contracts have several limitations. For example, they struggle to deal with the class imbalance problem, and their analysis of function call transactions is inadequate, resulting in redundant features. To tackle the challenges of detecting Ponzi contracts, we present PonziFinder, a novel approach that leverages convolutional-based edge-enhanced graph neural network and attention mechanism for the classification of contract transaction graphs. In contrast to previous methods, we not only consider transaction value and timestamp but also analyze transaction input to standardize and sort transactions. We extract node and edge features that capture the unique characteristics of Ponzi contracts. The edge feature, reflecting interaccount correlation, enhances the propagation and updating of node features for effective Ponzi contract detection. To prevent oversmoothing of node embedding caused by the shallow transaction graph and extract important account node information, we introduce an attention-based global layerwise aggregation mechanism (ALGA) for generating the final contract graph representation for classification. Moreover, we optimize the node feature set and use an effective strategy based on undersampling and ensemble learning to address the issue of class imbalance. Experimental results show that PonziFinder can detect all types of Ponzi contracts (100%) with 97% accuracy when there is sufficient transaction data, outperforming other models. The analysis of input values and the ALGA mechanism are experimentally shown to improve accuracy by 4% and 2%, respectively. In summary, PonziFinder is a novel and effective method for detecting Ponzi contracts. Our approach addresses the limitations of existing methods and demonstrates significant improvements in accuracy and efficiency.