Enhancing Smart Contract Vulnerability Detection using Deep Learning

Abstract

This paper investigates the application of CodeBERT, a pre-trained transformer model, to improve the detection of vulnerabilities in smart contracts.Smart contracts, while central to blockchain technology, are susceptible to security flaws that can result in significant financial and operational risks.By fine-tuning CodeBERT on labeled datasets specifically curated for smart contracts, our approach enhances the precision and efficiency of identifying various security issues.This method not only offers a robust solution to the existing challenges in blockchain security but also contributes to the broader efforts to secure decentralized systems and ensure the reliability of blockchain applications