Enhancing Smart-Contract Security through Machine Learning: A Survey of Approaches and Techniques

Abstract

As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain.

Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain

“…These contracts have completely transformed the way in which online financial transactions are processed. Because of its ability to run decentralized applications (DApps) and its smart contracts, Ethereum [2] has emerged as a core technology for the modern distributed digital economy. The size and complexity of the Ethereum network make it an appealing target for cybercriminals, despite the fact that it is a popular cryptocurrency.…”