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Exploring Deep Learning for Vulnerability Detection in Smart Contracts

Journal Title
Keywords
vulnerability detection
 blockchain
 Smart contracts
 ethereum
Readers/Advisors
Spetka, Scott Ph.D., Adriamanalimanana, Bruno Ph.D., Chiang, Chen-Fu Ph.D.
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Term and Year
Spring 2022
Publication Date
2022-05
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SUNY Polytechnic Institute College of Engineering
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SCVUtter.pdf
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UtterSig06012022(1).pdf
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http://hdl.handle.net/20.500.12648/8620
Abstract
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.
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