SCVD-SA: A Smart Contract Vulnerability Detection Method Based on Hybrid Deep Learning Model and Self-attention Mechanism

Abstract

With the continuous development of blockchain technology, smart contracts have found widespread application in various fields of production and daily life. However, as the number of smart contracts increases, so do the economic losses caused by vulnerabilities in these contracts. Consequently, ensuring the security of smart contracts has become a topic of great concern. Unfortunately, existing techniques for detecting smart contract vulnerabilities are insufficient. These detection methods heavily rely on fixed expert rules, leading to low detection accuracy and time-consuming processes as the complexity of smart contracts increases. A smart contract vulnerability detection methodology named SCVD-SA is proposed in this paper to address this issue. This model utilizes a hybrid deep learning approach and incorporates a self-attention mechanism. By combining Word2Vec word embeddings with various deep learning models, the model can extract features effectively. The introduction of a self-attention mechanism further enhances the model's ability to assign greater weights to more important features. Ultimately, these features are utilized for smart contract vulnerability detection. The proposed SCVD-SA method has been extensively evaluated on the public dataset SmartBugs Dataset-Wild, and the results demonstrate its superiority over several of the latest smart contract vulnerability detection methods in terms of detection effectiveness and stability. The detection accuracy for Callstack deep attack vulnerability and timestamp dependency vulnerability reaches 91.65% and 94.68%, respectively. Moreover, the detection accuracy for integer overflow vulnerabilities has also significantly improved, reaching 93.39%. Notably, SCVD-SA surpasses existing state-of-the-art models by 3.65% in detecting the widely studied reentrancy vulnerabilities.