Application of machine learning algorithms to enhance blockchain network security
This paper embarks on a detailed examination of the inherent security challenges faced by blockchain networks, including fraudulent transactions, double-spending, and 51% attacks, among others. Using recent advancements in ML, it presents a novel methodology for real-time anomaly detection, predictive threat modeling, and adaptive security protocols that leverage data-driven insights to fortify the blockchain against both known and emerging threats. By analyzing case studies and empirical data, this study illustrates the effectiveness of ML techniques in enhancing the