Deep learning-based solution for smart contract vulnerabilities detection.

ABSTRACT: This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the tra

SUBMITTER: Tang X 

PROVIDER: S-EPMC10654660 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature