We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.
Rossini, M., Zichichi, M., Ferretti, S. (2022). Smart Contracts Vulnerability Classification through Deep Learning [10.1145/3560905.3568175].
Smart Contracts Vulnerability Classification through Deep Learning
Rossini, Martina;Zichichi, Mirko;Ferretti, Stefano
2022
Abstract
We investigate the use of deep learning to classify smart contract code vulnerabilities. We use different variants of Convolutional Neural Networks (CNNs) and a Long Short-Term Memory (LSTM) neural network. Five classes of vulnerabilities were employed. Our results suggest that the CNNs are able to provide a good level of accuracy, thus showing the viability of the proposed approach.File in questo prodotto:
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