A Study on Smart Contract Vulnerability Detection Based on Transfer Learning - M. Poojari, Sayali; S. Sonawane, Swapnil (Journal of Neonatal Surgery, 2025)
This paper provides an analysis of the security threats which are present in blockchain smart contracts, namely the vulnerabilities that include re-entrancy, integer overflow, denial of service, and access control. Using various detection tools like Mythril, Slither, Oyente, and Securify, along with their limitations (false positives) and emphasizes the need for fortitude in security measures, since only growing levels of awareness will bolster further development of blockchain solutions. It classifies smart contract vulnerabilities using a three-module technique based on data from the Ethereum documentation and the Smart Contract Dataset repository. The method comprises extracting bytecode from Solidity files, creating images, and building three deep learning models: CNN, XCEPTION, and EfficientNet-B2. The Convolutional Neural Network (CNN) is the most effective technique, with an overall accuracy of 71 percent. XCEPTION and EfficientNet-B2 yield similar accuracy rates of 69 and 75 percent, respectively. The work contributes to our understanding of smart contract security and aims to reduce vulnerabilities in Ethereum smart contracts.