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mesut-ozdag / repository
Application of Logistic Regression and XGBoost using Jupyter Notebook to predict smart contract vulnerability categories from features extracted from Solidity source code.
For this project, SmartBugs-Curated is used, which is public on GitHub. It was created as a curated dataset of Solidity smart contracts annotated with tagged vulnerabilities, and SmartBugs’ own dataset documentation lists it as 143 curated vulnerable contracts. A related description also notes 10 DASP-derived vulnerability categories, which makes it a good fit for a multi-class supervised learning lecture rather than a simple safe-vs-vulnerable binary task.
https://github.com/smartbugs/smartbugs-curated
SmartBugs-Curated:
• multi-class classification of vulnerability type
• feature extraction from Solidity code
• Logistic Regression vs XGBoost comparison
Selected from shared topics, language and repository description—not editorial ratings.
bruk19 /
This is a decentralized supply chain management application built with Next.js and Solidity Ethereum smart contracts. It allows tracking of products through various stages of the supply chain, from raw material supply to retail sale.
bruk19 /
This is a decentralized supply chain management application built with Next.js and Solidity Ethereum smart contracts. It allows tracking of products through various stages of the supply chain, from raw material supply to retail sale.