Vulnerability Detection Method Based on Deep Fusion of Multi-dimensional Features from Heterogeneous Contract Graphs

Smart contracts are pieces of code that execute automatically on the blockchain,and the safety problem is critical due to their irreversibility and close links to financial transactions.However,the current smart contract vulnerability detection technology still faces problems such as low feature extraction efficiency,low detection accuracy,and over-reliance on expert rules.In order to solve these problems,this paper proposes a vulnerability detection method based on multi-dimensional feature deep fusion of heterogeneous contract graph.Firstly,the code of smart contract data is denoised,and the data set is expanded by data enhancement method of code function exchange,and represented as heterogeneous contract graph.Secondly,the high-dimensional semantic representation of nodes in the smart contract graph is efficiently obtained by combining graph embedding technology and code pre-training technology.Finally,the dual heterogeneous graph attention network is designed to deeply integrate the node features learned in two dimensions to achieve more accurate vulnerability detection.The experimental results for different types of vulnerabilities show that the overall performance of the proposed method has been improved,and the average F1 index is higher than 77.72%.In the case of denial of service vulnerability detection,the F1 value is up to 84.88%,which is significantly improved by 10.62% and 22.34% compared with the traditional deep learning method and the graph topology detection method respectively.The proposed method not only improves the detection efficiency and accuracy,but also reduces the dependence on expert rules by learning node characteristics,providing a more reliable guarantee for the security of smart contracts.