Smart Contract Vulnerability Detection Method Based on Deep and Cross Network with Feature Aggregation

The advent of decentralised applications across a range of sectors has led to a growing emphasis on the research and development of methods to identify vulnerabilities in smart contracts for decentralised applications. However, current detection techniques have been found to have limitations in terms of accuracy and the number of false alarms they generate. In order to address the aforementioned issues, this paper puts forth a modular vulnerability detection model, designated as BAMC. The method initially utilises the word2vec model to derive the word vector representation of the smart contract, subsequently extracting the word order information through a bidirectional long short-term memory network. Subsequently, the attention mechanism and max-pooling operation are employed to process the word order information, thereby obtaining fine-grained features and key features. Ultimately, explicit bounded-degree feature interactions are achieved through the combination of deep and cross networks, thus enabling the detection of reentrancy vulnerabilities and timestamp vulnerabilities. The experimental results demonstrate that the proposed method exhibits superior performance in comparison to existing techniques, with significantly higher values for various indexes. Notably, the reentrancy vulnerability and the - of timestamp vulnerability reach 86.14 and 91.43 , respectively.