Exploring the Role of Adaptive Machine Learning in Blockchain Security and Cyber Threat Intelligence

Abstract

This paper investigates the role of adaptive machine learning (AML) in enhancing blockchain security and cyber threat intelligence. As blockchain technology becomes increasingly integrated into various sectors, the security challenges associated with its use have intensified, making traditional security measures inadequate. Adaptive machine learning offers a dynamic solution, enabling real-time analysis and response to emerging threats. By employing algorithms that learn and evolve from historical data, AML can identify anomalous patterns indicative of potential security breaches and fraud in blockchain networks. This research outlines the mechanisms through which AML can enhance threat detection, improve the resilience of blockchain systems, and facilitate more effective cyber threat intelligence sharing. Furthermore, the study examines case studies that illustrate successful AML applications in real-world blockchain environments, highlighting the technology's effectiveness in mitigating risks. Ultimately, this paper contributes to the understanding of how integrating AML with blockchain can lead to more secure digital infrastructures.