Vulnerability Detection of Smart Contracts Based on Data Flow Graph and Hybrid Network Model
Smart contracts control the flow of substantial assets on the blockchain,making their security crucial.Accordingly,this paper proposes a method called DFG-HDP(Data Flow Graph and Hybrid Deep Learning Model)for detecting vulnerabilities in smart contracts.This method first cleans and normalizes variables in the smart contract source code.Secondly,it extracts data flow features from the source code and combines them with the source code for input.Finally,it integrates various word embedding models with different deep learning models to learn and detect vulnerabilities from the input.Experimental results indicate that this method achieves an F1 score of 89.90%in smart contract vulnerability detection,outperforming the previous vulnerability detection method CBGRU.This result demonstrates the effectiveness and superiority of the proposed method.
smart contractsvulnerability detectiondata flow graphhybrid model
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