Digital Twin for Cybersecurity Incident Prediction: A Multivocal Literature Review

Abstract

The advancements in the field of internet of things, artificial intelligence, machine learning, and data analytics has laid the path to the evolution of digital twin technology. The digital twin is a high-fidelity digital model of a physical system or asset that can be used e.g. to optimize operations and predict faults of the physical system. To understand different use cases of digital twin and its potential for cybersecurity incident prediction, we have performed a Systematic Literature Review (SLR). In this paper, we summarize the definition of digital twin and state-of-the-art on the development of digital twin including reported work on the usability of a digital twin for cybersecurity. Existing tools and technologies for developing digital twin is discussed.