Date of Publication :2nd September 2024

Abstract:As the adoption of Blockchain technology continues to grow, ensuring the security of smart contracts has become a critical concern. Traditional methods of securing smart contracts, such as static analysis and formal verification, have shown limitations in terms of scalability, accuracy, and the ability to adapt to evolving threat landscapes. This paper explores the innovative application of machine learning (ML) techniques to enhance the security of smart contracts. We investigate several tools and techniques that apply machine learning to smart contract vulnerability identification anomalous behavior detection and threat prediction. We investigate a range of ML tools and methodologies, including anomaly detection, classification algorithms, and reinforcement learning, to identify and mitigate vulnerabilities more effectively. Additionally, we discuss the integration of ML-based tools with existing security frameworks to create a comprehensive, adaptive security system for smart contracts. At last is suggested that now focus should be more on developing an ML model that integrates with the present tools to give better results.

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