AI in Blockchain Security: Detecting Anomalies in Smart Contracts

Abstract

Blockchain technology is popular for its safe and decentralized architecture that help build digital contracts, supply chain management, and financial services applications. The key features of this technology is that smart contracts automatically perform agreements when certain predefined conditions are met. Coding errors or design flaws make them vulnerable to security attacks. People with malicious intent may exploit these flaws, resulting in significant financial losses and a decline in trust in blockchain technology as a whole. With help of artificial intelligence and machine learning algorithms this paper identifies anomalies in smart contracts and increase blockchain security by its mitigation. The machine learning algorithm is trained to identify anomalies that deviate from standard norms by analyzing patterns and behavior. The paper focuses on identifying common vulnerabilities that attackers may exploit like overflows, unauthorized access channels and re-entrancy bugs using a gradient boosting classifier. Developers and other stakeholders can be informed about possible security vulnerabilities before they become serious threats by using anomaly detection to generate early alerts. The primary goal of this research is to create a strong and reliable security tool that will significantly improve blockchain security and guarantee that smart contracts operate as intended and increase public confidence in blockchain technology.