Ponzi Scheme Detection Based on Control Flow Graph Feature Extraction

The blockchain ecosystem is expanding as a result of advancements in blockchain technology and the emergence of BaaS (Blockchain as a Service) platforms. Smart contracts are designed to carry out diverse business operations, but there is a risk of Ponzi schemes being concealed within them. These schemes masquerade as investment agreements and deceive users, resulting in substantial losses for the blockchain community. Detecting Ponzi schemes in smart contracts is crucial. This study introduces a machine learning approach to identify Ponzi schemes by extracting features from smart contracts using the control flow graph. During the construction of the control flow graph for the smart contract’s bytecode, elements unrelated to its functionality are identified and eliminated. We utilize the control flow graph to extract n-gram Term Frequency and n-gram Term Frequency-Inverse Document Frequency features. These features are respectively employed to construct a Random Forest model for Ponzi scheme detection. To address the issue of imbalanced samples, the SVM_SMOTE oversampling algorithm is applied to balance the number of positive and negative samples. The results from experiments conducted on a real-world dataset demonstrate the effectiveness of our approach. The feature extraction method based on the control flow graph outperforms the method based on continuous text. Additionally, the Random Forest model utilizing SVM_SMOTE outperforms four existing models.