A Comprehensive Review on Cybersecurity of Digital Twins Issues, Challenges, and Future Research Directions

This systematic literature review examines existing literature to determine how Digital Twin (DT) technology can enhance security, with an emphasis on emerging issues, approaches, and proposals in cybersecurity. The review categorizes selected studies based on the techniques used to protect DTs: Machine Learning (ML) and non-machine learning techniques. DTs are classified into five types: component level, process level, asset level, system level, and network of systems level, each with unique applications and corresponding cybersecurity threats. The main conclusions highlight medium to very high perceived risks depending on the DT type and industry implementation. Results identify the most common types of attacks on DTs, various security enhancement techniques, and the sectors that utilize DTs. Future research should focus on developing more effective security paradigms, technologies, and integration schemes to reduce risks and maximize DT technology's potential. This review presents the current status of DT-related cybersecurity and explores ways to enhance security against emerging cyber risks.