Detection of Security Vulnerabilities in Smart Contracts
| dc.contributor.author | Ghorab, Djamel Eddine Hakim | |
| dc.contributor.author | Mokhati, Farid | |
| dc.date.accessioned | 2024-10-17T20:11:08Z | |
| dc.date.available | 2024-10-17T20:11:08Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Machine learning, Blockchain technology, and Cybersecurity are topics that have garnered significant interest from researchers. Smart contracts, programs that operate on the Ethereum Blockchain, have demonstrated extensive utility across various domains. However, with their increased usage comes an elevated risk of hackers exploiting these contracts. In this thesis, we propose the use of BERT, a machine learning model, for detecting vulnerabilities in Solidity smart contracts. Our work involves fine tuning a pre-trained deep learning model to predict whether a contract is vulnerable. Furthermore, we develop an extension for an Integrated Development Environment (IDE) that utilizes the trained model, assisting developers in enhancing the security of smart contracts. | |
| dc.identifier.uri | http://dspace.univ-oeb.dz:4000/handle/123456789/20055 | |
| dc.language.iso | en | |
| dc.publisher | University of Oum El Bouaghi | |
| dc.title | Detection of Security Vulnerabilities in Smart Contracts | |
| dc.title.alternative | A Deep Learning-Based Approach | |
| dc.type | Other |
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