Smart Contract Vulnerability Detection Model Based on Supervised Bi-PCA

Abstract

Smart contracts manage billions of dollars' worth of digital assets. Once vulnerabilities are exploited, they may lead to fund theft, transaction rollback, or asset freezing. The current machine learning based smart contract vulnerability detection methods have poor performance and high time complexity in detecting sparse labeled data. We propose a smart contract vulnerability detection model for supervised Bigram-Principle Component Analysis (Bi-PCA). Supervising Bi-PCA can utilize labeled vulnerability data for supervised dimensionality reduction. The supervised Bi-PCA model can utilize the information from these additional labels to accurately extract more interpretable potential structures. This detection model is universal and can be used in industrial scenarios on all labeled datasets. The experimental results show that it has high accuracy and recall while maintaining the feature of the original label data.