Smart contracts auditing and multi-classification using machine learning algorithms: an efficient vulnerability detection in ethereum blockchain

Abstract

The adoption of Smart Contracts has revolutionized industries like DeFi and supply chain management, streamlining processes and enhancing transparency. However, ensuring their security is crucial due to their unchangeable nature, which makes them vulnerable to exploitation and errors. Neglecting security can lead to severe consequences such as financial losses and reputation damage. To address this, rigorous analytical processes are needed to evaluate Smart Contract security, despite challenges like cost and complexity associated with current tools. Following an empirical examination of current tools designed to identify vulnerabilities in Smart Contracts, this paper presents a robust and promising solution based on Machine Learning algorithms. The objective is to elevate the auditing and classification of Smart Contracts, building trust and confidence in Blockchain-based applications. By automating the security auditing process, the model not only reduces manual efforts and execution time but also ensures a comprehensive analysis, uncovering even the most complex security vulnerabilities that traditional tools may miss. Overall, the evaluation demonstrates that our proposed model surpasses conventional counterparts in terms of vulnerability detection performance, achieving an accuracy exceeding 98% with optimized execution times.