Secure Aspect of Digital Twin For Industry 4.0 Application Improvement Using Machine Learning

Abstract

With the new paradigm of digital manufacturing and the idea of Industry 4.0, it is now possible to combine the progress made in manufacturing with the latest information and communication technology. The digital twin is one of the most interesting and maybe even game-changing technologies for smart manufacturing and Industry 4.0. (DT). "Digital Twin" is a term for a digital copy of a physical object that can be used to manage that physical object (from simple monitoring to autonomy). If you think of DTs as a way to share data between real and virtual machines without any problems, you'll understand them better. Using digital simulation tools in manufacturing systems can cut the amount of time and money spent on production while also making production faster, more flexible, and better. With the help of sensory data used in digital simulations, production planning and execution can be made more efficient and safer, and consumers' trust in the systems they depend on can grow. Digital Twin can provide an accurate environment so that a security study can be done or possible ways to protect against certain scenarios can be tested. The aviation, industrial, and automotive industries all use digital twin technology a lot. These and other businesses could use this technology to make their businesses safer and more reliable. The digital twin lets users test, deploy, and look at how well attacks and defenses work in the real world. Even though digital twin technology has come a long way in the past few years, it is still not being used to its full potential. Here, we look at cyber-security, how systems are vulnerable, and how digital twins can be used to reduce these risks and become an important part of a security in-depth defence. The authors did a thorough review of previous research on this topic to figure out how to group the current studies on digital twins. Cyber security in smart cities, for example, is one of the most important and recent DT advances and applications that have been looked at. Other groups include current research problems and likely areas for future research. The researchers found that the Multivariate Linear Regression method for supervised learning could be used to automate the model and predict how well it will work when the features change.

Keywords: Artificial Intelligence, Digital Twin, Cyber-physical system, Machine learning, Industry 4.0, Data Science, IIoT, Multivariate Linear Regression