Towards Secure Smart Contracts: An AI-Enhanced Framework for Vulnerability Detection

Smart contracts, serving as self-executing programs on blockchain platforms, have emerged as a key innovation for enhancing data security. Despite advancements in both blockchain technology and smart contracts (SCs), Ethereum-based SCs remain vulnerable to security breaches. Exploitation of these vulnerabilities can result in substantial financial losses for both service providers and users. Consequently, the detection and mitigation of security vulnerabilities in smart contracts are critical to ensuring the security and reliability of blockchain platforms. Machine learning approaches are emerging as effective alternatives to traditional vulnerability detection methods, though many rely heavily on expert knowledge and primarily target familiar vulnerabilities. This chapter explores the creation of an AI-driven framework for detecting vulnerabilities in smart contracts, aimed at reducing risks and improving the reliability of blockchain systems. By incorporating advanced Machine Learning (ML) and Deep Learning (DL) techniques, the framework seeks to improve the accuracy and efficiency of vulnerability detection, addressing the shortcomings of traditional static and dynamic analysis methods. The proposed approach not only strengthens the security of smart contracts but also contributes to the broader goal of building more resilient and reliable blockchain ecosystems. Through an in-depth analysis of methodologies and case studies, this chapter highlights the essential role of AI in advancing the secure development and deployment of smart contracts.