A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence

TL;DR

This paper surveys digital twins (DTs) within Industry 4.0 and their security implications, focusing on how artificial intelligence can bolster DT cybersecurity while acknowledging the risks AI introduces. It maps DT concepts, architectures, and the attack surface across CPS/IoT, and categorizes security and privacy challenges by application (Smart Cities, Smart Health, Smart Cars). The authors analyze threats, including reconnaissance, simulation-based manipulation, and DT-targeted attacks, and discuss AI-based defenses such as explainable AI, ML/DL-driven intrusion detection, and blockchain-enabled integrity. The work provides a roadmap for researchers and practitioners to design secure DT-enabled systems, highlighting the need for standardization, robust defense-in-depth, and governance to harness the benefits of DTs without compromising security.

Abstract

The potential of digital twin technology is yet to be fully realized due to its diversity and untapped potential. Digital twins enable systems' analysis, design, optimization, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitization, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

Figures (8)

• Figure 1: The relationship between DT, CPS, and IoT
• Figure 2: DT applications and Scopes (Adapted from source: Microsoft Azure Blog on Digital Twins)
• Figure 3: DT applications
• Figure 4: DT generations
• Figure 5: Four various DTs