Smart Contract Vulnerability Detection Method based on Bi-LSTM Neural Network

Abstract

The traditional smart contract defect detection method needs to generate corresponding knowledge according to its content when discovering new defects, and then the professionals build assertions and load them into the platform for defect detection. Due to existing methods are prone to loss or misdetection of defects due to human subjective consciousness, a smart contract vulnerability identification method based on Bi-LSTM neural network is proposed. The method firstly vectorizes the smart contract code, then inputs the vectorized data into the LSTM network to generate a model, and finally uses this model to detect defects. Experiments show that this method has a high defect detection rate for different types of new defects, and it is enough to be applied to practical scenarios.