A Comparison of Machine Learning Techniques for Ethereum Smart Contract Vulnerability Detection

Vulnerability detection is particularly relevant in smart contracts, where modifying the code after deployment is impossible. Machine learning solutions provide greater efficiency than static analyzers in speed and detection. This study evaluates various classic machine-learning techniques and state-of-the-art neural networks for training a vulnerability detector. We analyze the largest and most reliably labelled dataset of smart contracts currently available, experimenting with six data representations of smart contracts and a multimodal approach. Our experiments show that both deep and traditional machine learning methods excel in different scenarios. Notably, eXtreme Gradient Boosting achieved an F1-score of 0.91 with the multimodal approach, which suggests its potential for more robust classification. At the same time, the results underscore the need for larger datasets to showcase the full potential of the evaluated methods.