An integrated deep learning model for Ethereum smart contract vulnerability detection

An integrated deep learning model for Ethereum smart contract vulnerability detection

연구 분야: Strategies

학회: International Journal of Information Security

Smart contracts are utilized widely in developing safe, secure, and efficient decentralized applications. Smart contracts hold a significant amount of cryptocurrencies, and upgrading or changing them after deployment on the blockchain is difficult. Therefore, it is essential to analyze the integrity of contracts to design secure contracts before deploying them. As a result, the effective detection of various class vulnerabilities in smart contracts is a significant concern. While human specialists are still necessary for vulnerability detection methods that utilize machine learning and deep learning, these approaches often miss numerous vulnerabilities, leading to a significant false-negative rate. This research proposes a two-step hierarchical model using deep learning techniques that significantly improve the feature extraction mechanism for Ethereum smart contracts to circumvent these limitations. The first step is to determine the relationship between opcodes using a transformer for extracting the internal features of contracts to strengthen the contextual information. Then, a Bi-GRU is employed to aggregate forward and backward sequential information for long-term reliance, including vulnerable code. In the second step, the Text-CNN and spatial attention extract the local features to emphasize the significant semantics. Experiments conducted on 49,552 real-world smart contracts have demonstrated that the proposed method is more effective than state-of-the-art methods. Extensive ablation experiments are carried out to additional illustrate the framework design option's efficacy.