Comparative Analysis of Smart Contract Vulnerability Detection: Traditional RegEx vs DL Codebert Model

Abstract

Smart contracts are a crucial component of blockchain systems, enabling high programmability and trusted transactions without the need for third parties. Their extensive implementation has significantly enhanced transparency and transactional efficiency in blockchain ecosystems. However, this advancement has also raised considerable concerns about vulnerabilities in smart contracts, which may lead to the hacking of blockchain applications, resulting in significant financial losses. This study suggests a traditional approach using Python regular expressions alongside the CodeBert deep learning model for smart contract vulnerability detection. Additionally, it compares the results of both approaches based on the number of instances. CodeBert emerges as an efficient deep learning model by detecting around 90.1 % of smart contract vulnerabilities.