An interpretable model for large-scale smart contract vulnerability detection

Abstract

Smart contracts hold billions of dollars in digital currency, and their security vulnerabilities have drawn a lot of attention in recent years. Traditional methods for detecting smart contract vulnerabilities rely primarily on symbol execution, which makes them time-consuming with high false positive rates. Recently, deep learning approaches have alleviated these issues but still face several major limitations, such as lack of interpretability and susceptibility to evasion techniques. In this paper, we propose a feature selection method for uplifting modeling. The fundamental concept of this method is a feature selection algorithm, utilizing interpretation outcomes to select critical features, thereby reducing the scales of features. The learning process could be accelerated significantly because of the reduction of the feature size. The experiment shows that our proposed model performs well in six types of vulnerability detection. The accuracy of each type is higher than 93% and the average detection time of each smart contract is less than 1 ms. Notably, through our proposed feature selection algorithm, the training time of each type of vulnerability is reduced by nearly 80% compared with that of its original.