Machine learning methods for detecting smart contracts vulnerabilities within Ethereum blockchain: A review

This paper presents a comprehensive exploration of the intersection between machine learning and smart contract vulnerabilities on the Ethereum blockchain. Introduced by Vitalik Buterin in 2015, Ethereum stands as a prominent blockchain network, necessitating innovative approaches to secure smart contracts against vulnerabilities and potential attacks. This research follows PRISMA guidelines, posing three fundamental questions and conducting a meticulous literature review. The study categorises machine learning applications into seven distinct groups, analysing their taxonomy, feature types, and engineering methods. The findings indicate a dynamic landscape characterised by a noticeable trend towards increased complexity. This complexity is evident not only in the integration of machine learning frameworks that combine different architectures of deep learning models, such as Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), or Recurrent Neural Networks (RNN), but also in the incorporation of various types of data related to smart contracts (SCs). The discussion dissects the advantages, limitations, and future directions in securing smart contracts using machine learning. The paper concludes by emphasising the evolving role of machine learning in strengthening the Ethereum blockchain, fostering trust, and enhancing security in decentralised systems.