Machine Learning Application for Virtual Replicas (Digital Twins) in Cybersecurity

Abstract

In the swiftly evolving realm of technology and cybersecurity, safeguarding our digital assets is paramount. This study explores the integration of machine learning techniques with virtual replicas, or digital twins, under the proposed system name CyberGuard, aiming to fortify cybersecurity measures and proactively prevent potential threats. Digital twins serve as virtual counterparts to real-world systems, providing a comprehensive understanding of their behavior. The research specifically concentrates on leveraging machine learning algorithms within CyberGuard to enhance the capabilities of digital twins in identifying and mitigating cyber threats. Through advanced analytics, this intelligent system can adapt to evolving cyber risks, identify unusual activities, and predict potential security breaches. The results highlight that the synergy between machine learning and Virtual Replicas not only improves threat detection and response times but also continuously strengthens the overall resilience of our cybersecurity infrastructure.