A Hybrid Approach to Cybersecurity: Integrating Machine Learning and Blockchain Technologies

Abstract

In the evolving landscape of cybersecurity, traditional defense mechanisms often fall short in addressing sophisticated and emerging threats. This paper presents a hybrid approach that integrates machine learning (ML) and blockchain technologies to enhance cybersecurity. Machine learning, with its capabilities in anomaly detection, predictive analytics, and automated responses, offers advanced solutions for identifying and mitigating cyber threats. Blockchain technology, renowned for its decentralized nature and immutability, ensures data integrity and transparency, addressing the weaknesses inherent in centralized systems. By combining these technologies, the hybrid approach aims to leverage ML’s analytical power alongside blockchain’s secure data storage and tamper-proof records. This integration enhances threat detection, improves data quality, and provides a robust framework for real-time threat response and system integrity. The paper explores the synergistic benefits of this hybrid model, outlines an implementation framework, and discusses potential applications across various sectors. It addresses challenges such as scalability and computational overhead, and outlines future research directions to optimize and refine this approach. This integrated strategy represents a significant advancement in cybersecurity, offering a more resilient and adaptive defense against the growing complexity of cyber threats.