Exploring Deep Learning for Vulnerability Detection in Smart Contracts
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Author
Utter, ColbyReaders/Advisors
Spetka, Scott Ph.D.Adriamanalimanana, Bruno Ph.D.
Chiang, Chen-Fu Ph.D.
Term and Year
Spring 2022Date Published
2022-05
Metadata
Show full item recordAbstract
This project explores vulnerability detection in Solidity smart contracts. The following report provides a brief overview of blockchain technology, smart contract specific vulnerabilities and the tooling that exists to detect these vulnerabilities. The application of deep learning as a vulnerability detection tool was explored in more detail. The result of this work is an LSTM trained to detect re-entrancy vulnerabilities in smart contracts. The model is trained on smart contracts identified and labeled in the ScawlD dataset provided by Yashavant et al.