Abstract
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 resilience and integrity of blockchain systems. Furthermore, it explores the implications of these innovations for future blockchain applications, proposing a framework for the integration of ML into blockchain security strategies. This article aims to serve as a cornerstone for researchers, technologists, and cybersecurity professionals, offering insights into the future of secure blockchain ecosystems powered by the intelligent capabilities of machine learning.
Published Version
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