The Digital Twin for Cyber-Physical Systems Security (CPSS)
Organizations need to have a clear vision for the future of digital technology and its impact on their business. The Digital Twin concept represents both a physical and a virtual world in which industrial products receive dynamic digital representations.
While the term “digital twin” typically refers to a data-driven physical model of a system, it can also be used to describe an emulated or simulated device that may be connected to an emulated network.
The Digital Twin is a computer model that reflects and simulates the real object and its interactions with its surroundings, providing a more accurate representation of the shape and shape of the object than a physical replica. The method of replicating physical objects is still in its infancy, and it requires specific technology and equipment to succeed.
Digital twins are simple; they act as a bridge between the physical and digital worlds by using sensors to collect real-time data on physical objects. This data is used to create digital duplicates of these objects, which make it possible to understand, analyze, manipulate, and optimize them.
The digital twins, combined with other technologies such as cloud computing, artificial intelligence, and machine learning, will track, monitor, and optimize the flow of goods from factories around the world. Increased availability of cloud computing, analytics, and data analytics services is a key factor for digital twins. With cloud-based platforms for IoT and analytics software, companies are investing in cloud-based analytics platforms that enable them to capitalize on the “digital twin” trend. These investments are designed to optimize the integration of data from various sources such as the cloud, mobile devices, cloud services, data centers, or even physical systems.
To create a digital twin of a physical asset, engineers collect and synthesize data from a variety of sources, including sensors and physical components of physical assets such as buildings, cars, vehicles, and other physical objects. This information and AI algorithms are integrated into a virtual model based on physics. By applying analytics to this model, we obtain the best possible analysis and insights about the asset. The consistent flow of data helps us to gain relevant insights about the assets and use them correctly, which contributes to optimizing business results.
The concept of creating twins as tools to improve decision-making has long been used in engineering. Multiple technologies, including computer vision, artificial intelligence, machine learning, and advanced simulation, make it possible to create a virtual version of a physical asset using the same physical components, sensors, and components. Advanced simulations can use the largest and most complex scales and work in real-time to solve the most important problems in the design, construction, operation, and maintenance of complex systems.
A fully developed digital replica can be built in real-time, as used for future testing, development, and experimentation. Simply put, this is a digital replica clone that allows manufacturers to interact with digital platforms and perform tests on real physical twins in reality.
Currently, the most efficient way to do this is to attach structural sensors that act as boundaries to help the digital platform accurately replicate shape, and object.
By iteratively modeling changes and testing the functioning of components and systems, digital twins can save time and money by cost-effective troubleshooting and correcting disruptions in the virtual world in real-time. More than just visualization, the digital twin can build consensus and accelerate innovation. Connected digital things generate real-time data, and this helps companies predict problems early, warn early, prevent downtime, develop new opportunities, and plan better products for the future, even at a lower cost than simulation.
Similar to the advantages of manufacturing, digital twins can revolutionize the design and production of powerful, high-performance components for industrial applications.
For its Apollo program (around 60s and 70s), NASA has developed two identical spacecraft that reflect conditions in space and on Earth for training and flight preparation.
The digital twin is also a component of cyber-physical systems. The digital twin model enables the integration of physical and virtual physical systems into the cyber-physical security model of a cyber-system.
The introduction of CPPS (Cyber-Physical Production Systems) enables smart factories to assist in various decision-making processes by predicting the future based on past and present situations. CPPS consist of independent and cooperative elements integrated into the Industrial Internet of Things to create Smart Factories, which face efficiently to demands of the multifaceted MS (Manufacturing Systems) control. IoT refers to wireless communication capabilities integrated with sensors and computers that allow data to be collected on uniquely identifiable objects over the Internet. CPPS, one of the few core technologies needed to build a smart factory.
In these futuristic factories, connected devices can sense their surroundings and interact with each other to make decentralized decisions. This is similar to the run-and-run simulation scenarios that are often seen in science fiction movies, where every possible scenario is proved.
Previous studies suggest that the common components of CPSs (Cyber-physical systems) and CPPs (Cyber-physical Platform) include electronic hardware, software, and digital twins that are interconnected in the real and virtual worlds. The digital twin concept can be applied by organizations to cyber-systems and physical OT systems by linking probabilistic engineering models with test, operational, and maintenance data to evaluate the security of their physical and virtual physical systems. Digital system models offer OT and IT engineers, stakeholders the opportunity to examine the potential risks and benefits of cyber systems that should be considered throughout the life cycle.
It also helps to conduct predictive assessments of future capabilities to influence the design and implementation of cyber-physical and virtual physical security solutions for cyber systems.
Designers need to figure out where things are going and how they should work before they are used physically. IoT deployment is digital twins optimized for both physical and digital security, as well as for the security of the devices themselves.
Essentially, the digital twin acts as a virtual version of the physical asset with the same physical components. Using data on how each part of a system reacts to the environment, supported by data provided by sensors in the physical world, its digital twins can simulate and analyze real-world conditions and respond to operational changes.
Modern aircraft engines can have thousands or tens of thousands of sensors and generate terabytes of data per second. In the coming years, it is expected the widespread use of digital twins in industries with multiple use cases. They could be digitally designed and tested before being physically manufactured. Market research firm Gartner has dubbed IoT a “digital twin” and estimates that digital twins will represent a billion things in three to five years. Organizations planning to use IoT should consider the digital twin as essential technology, as it provides a bridge between the physical and digital worlds. By 2021, GE will have more than $1.8 billion in annual revenue from its digital twin businesses.
In the context of our research, digital twins reflect the needs of experts in industrial automation who state the need for a high level of understanding of the physical and virtual components of a system. Since the digital twin runs in an isolated virtual environment, it can be analyzed without risking disturbances to living systems. Therefore, malicious behavior or errors may be indicated by the presence or absence of physical components within the virtual twin rather than by a physical component. An integrated virtual model means that conventional control systems can no longer be leveraged. We assume that the digital twins will not cause problems in the long term, but they will.