Abstract
In this paper, we investigate the use of deep learning techniques to identify and classify smart contract code vulnerabilities. We collected a large-scale dataset of smart contracts that we used to train different Convolutional Neural Networks (CNNs) models. In particular, we used two variants of 2-dimensional CNNs working on RGB images corresponding to contract byte-code, a 1-dimensional CNN working on the bytecode directly, and a Long Short-Term Memory (LSTM) neural network. Given a set of vulnerability detectors, we employed five classes of vulnerabilities. Our results show that CNNs provide a good level of accuracy and demonstrate the viability of using deep learning techniques to identify smart contract vulnerabilities.
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