Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for different vulnerabilities, which are used to extract feature graphs from the source code of smart contracts. Then, the node features in feature graphs are fed into a graph convolutional neural network for training, and the edge features are processed using a method that combines attention mechanism with gated units. Ultimately, the revised node features and edge features are concatenated through a multi-head attention mechanism. The result of the splicing is a global representation of the entire feature graph. Our method was tested on three types of data: Timestamp vulnerabilities, reentrancy vulnerabilities, and access control vulnerabilities, where the F1 score of our method reaches 84.63%, 92.55%, and 61.36%. The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.