Digital Twin Honeypots as a defense measure for the Internet of Things


Digital Twin systems are primarily systems that communicate and share data to generate a holistic knowledge of the systems’ operations and optimize decision making. Digital Twins are expected to transform the way industry runs, shifting from tight physical asset management to an increasingly automated, remote data-driven manner of operation. But there are significant risks if things go wrong: the threat of a cyber attack, supply chain fraud, blunders, delayed maintenance, and other difficulties all undermine the system’s integrity and undermine trust in the data it creates and consumes. This brings the need for a new way of thinking about security and trustworthiness in which risk and responsibility are shared, and actions by one have consequences for others [2].


Consider a digital twin a honeypot, in which we create a clone of our infrastructure to trick attackers into believing the system they’re breaking into is legitimate. We could utilize digital twins to examine cyber hazards and provide an extra layer of security to our network design. Using digital twins, you can recognize infrastructure as it is being constructed, allowing you to discover and minimize risk exposures. If any infrastructure modifications are required, you can anticipate and effectively manage risks before applying an upgrade. Attack graphs generated by cyber-intelligent digital twins may forecast the most successful path of the intruder. It may perform a step-by-step study to predict how an assault may manifest itself if it occurs. Critical data related to the company’s procedures may be used to assess the impact of risks. In the event of a breach, this will aid in determining the extent of the damage to both the functionality and operations levels. Organizations can utilize digital twins to monitor and assess security issues and calculate the gap between present and projected security standards. It could help determine the company’s specific security goals and prioritize cyber risks based on their impact on day-to-day operations. Before an infiltration effort turns into a cyber-attack, security staff may be able to make better decisions. Digital twins benefit from power, manufacturing, government, environmental cities, and the IT and OT sectors. To be effective, management must be dedicated to understanding and executing the value of a digital twin [3].


In the manufacturing industry, there is a growing emphasis on the digital twin to minimize costs and improve supply chain operations

Unplanned downtime and manufacturing waste have a significant impact on manufacturers. As a result, they are looking for a system that can predict potential flaws and breakdowns to save future losses. The usage of digital twins allows for the reduction of unnecessary time and expense in manufacturing. Engineers can optimize a product’s performance by incorporating digital twins by updating the physical prototype, which changes at every instance during the design phase. As a result, a digital prototype developed with a digital twin can be utilized to run simulations and be adjusted at any time in less time and a lower cost. A digital twin can be used in manufacturing for configuration management, asset management, process control, performance management, and simulation modelling. The data used in digital twin technology is historical in nature. In a few cases, the digital twin works with real-time data. Analysts can use digital twins to better understand the behaviour of a supply chain, anticipate unusual occurrences, and design an action plan to reduce costs and boost process efficiency. A digital twin can help businesses identify trends and simulate the consequences of changes to various processes to improve supply chain design testing and monitor risk and test probability. The digital twin provides a continuous, end-to-end image of all supply chain operations and bottlenecks, allowing manufacturers to handle problems faster and with less human intervention. These twins collect information to help identify potential problems in all delivery stages. A shipment digital twin, for example, will rely on data gathered from sensors that communicate updated data throughout the cargo, which can then be analyzed to detect performance and bottlenecks during transit and delivery journeys [1].


The importance of advanced real-time data analytics is growing.

Data and analytics are at the heart of digital twin technology. A digital twin can be used to produce twins of components, assemblies, people, or a whole manufacturing facility, which can then be merged in a variety of ways to build a solution with many data and information sources. The ability to observe and model a real-world object is a critical component of a digital twin. Furthermore, when an item can be monitored in the real world, it is possible to request or receive notifications about certain occurrences or changes. On the other hand, the ability to represent a real-world object is related to data such as identity, time, context, and events and hence correlates to a distinct physical thing. All of these digital twin aspects reflect real-time data analysis, which drives the operation of the digital twin in any company. Once established, digital twins and analytics enable more precise diagnostic, predictive, and optimal operations. After recognizing the benefits of AI and data analytics, businesses have begun to recognize the potential of digital twins [1].


The Automotive & transport industry to dominate the digital twin market during the forecast period

The automotive and transportation industries are expected to hold the greatest share of the digital twin market during the projected period. From 2022 to 2027, it is predicted to increase at a considerable CAGR. The increased use of digital twins for design, simulation, maintenance, repair, overhaul, production, and after-service can be attributed to the expansion. A digital twin can assess performance data gathered over time and under various conditions during vehicle design manufacturing. A digital twin, for example, allows visualization of a race car engine to determine the need for maintenance of components that can burn out or get damaged. Even after the transaction, a digital twin is utilized to collect feedback. Digital twins help to maintain vehicle safety by monitoring systems or parts that need to be replaced and alerting the appropriate teams about the change [1].


The digital twin market is predicted to increase from USD 6.9 billion in 2022 to USD 73.5 billion by 2027, at a CAGR of 60.6% between 2022 and 2027

The growing need for digital twins in the healthcare industry and the increased emphasis on predictive maintenance are driving the growth of the digital twin market. Because of the growing emphasis on digital twins in manufacturing industries to cut costs and enhance supply chain operations, the market has significant growth potential. Because of the closure of manufacturing plants in 2020, the outbreak and spread of COVID-19 caused a significant setback to export-oriented economies. The temporary shutdown of numerous industries, including automotive and transportation, aircraft, and infrastructure, impeded the expansion of the digital twin market. COVID-19 also caused procurement and supply delays in 2021. However, increased adoption of the digital twin in the healthcare and food and beverage industries mitigated the negative effects of the pandemic on industry growth in late 2020 and early 2021. Before COVID-19, there was an increased demand for smart city infrastructure in developing countries worldwide. With the pandemic’s negative effects on practically all sectors, new constructions, smart city projects, and smart infrastructure projects were temporarily halted.

Furthermore, several projects were postponed, and even when they were begun in 2020 and 2021, they were behind schedule. Considering these considerations, it is possible to conclude that worldwide market growth fell and slowed further throughout the pandemic period. However, the market will likely expand steadily following COVID-19 [1].



[1] “Digital Twin Market by Enterprise, Application, Industry and Geography – Global Forecast to 2027”

[2] “Digital twins break free of industrial roots”

[3] “Cyber-Attacks May Be Predicted Using Digital Twins”