A survey on ethereum smart contract vulnerability detection using machine learning

Onur Sürücü; Uygar Yeprem; Connor Wilkinson; Waleed Hilal; S. Andrew Gadsden; John Yawney; Naseem Alsadi; Alessandro Giuliano

doi:10.1117/12.2618899

30 May 2022 A survey on ethereum smart contract vulnerability detection using machine learning

Onur Sürücü, Uygar Yeprem, Connor Wilkinson, Waleed Hilal, S. Andrew Gadsden, John Yawney, Naseem Alsadi, Alessandro Giuliano

Author Affiliations +

Proceedings Volume 12117, Disruptive Technologies in Information Sciences VI; 121170C (2022) https://doi.org/10.1117/12.2618899
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.

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Onur Sürücü, Uygar Yeprem, Connor Wilkinson, Waleed Hilal, S. Andrew Gadsden, John Yawney, Naseem Alsadi, and Alessandro Giuliano "A survey on ethereum smart contract vulnerability detection using machine learning", Proc. SPIE 12117, Disruptive Technologies in Information Sciences VI, 121170C (30 May 2022); https://doi.org/10.1117/12.2618899

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