# Digital Twin Conceptual Model within the Context of Internet of Things
## 7 Application and Security Layers  
A large amount of data transmitted to the virtual space digital twin is inherent in a manufacturing process. This data can significantly help in improving the production efficiency, manufacturing flexibility, and visibility. This data can be turned into actionable insights with the help of manufacturing business intelligence tools and techniques that record all the activity for each machinery component and/or sensor. For example, the collected sensor data can be analyzed to provide predictive maintenance recommendations to reduce the machine downtime. Additionally, digital diagnostic and analytic results in VSL can be synthesized into meaningful dashboard charts, graphs, and reports. These dashboard reports can help identify important key performance indicators (KPIs) that are vital for industrial automation decisions and forecasts by different stakeholders. Different alerts and notification systems can be automated based on the processed results to empower the stakeholders to act quickly for addressing the underlying issues with the sensors/machinery component/system. Additionally, these reports can be further analyzed to determine weakness in the production with respect to design specifications and take necessary measures to mitigate them for the future. Depending on the application domain implementing a digital twin, it can add tangible value and answer key strategic questions for a variety of industries. One such use-case example of the automotive industry utilizing the proposed end-to-end conceptual model for digital twin is discussed in Section 8 of the paper. Another use-case example for application of proposed end-to-end conceptual model of digital twin in smart healthcare monitoring for mobile patients is in the scope of future work. As the digital twin's technology is becoming a strategic disruptive trend that is digitally transforming industrial and manufacturing industries, it is also attracting the interest of cyber attackers. Just like any smart technology, digital twins can contain vulnerabilities and encounter attacks at their various layers. Security is unfortunately overlooked in new technological trends. Therefore, organizations must be fully aware of the potential vulnerabilities and threats and be proactive in implementing the countermeasures. Each of the used layers in digital twins can be vulnerable to various attacks. Attacks on the physical layer include physical attacks against the used sensors, which include damaging, replacing, and stealing the sensors. Solutions for sensor authentication and validation are needed to ensure that the original sensors are not tampered with and are producing reliable data. Attacks on the communication layer include disrupting the service via denial of service attacks in addition to packet injection, sniffing, hijacking, and spoofing. This can impact the large, generated volume of real-time data that is collected and being transmitted to the digital twin. The transmitted data have to be secure and trusted with the use of cryptography. Firewalls, antivirus, and intrusion detection systems must be deployed to protect the system from external and internal attacks. Attacks against the application layer include buffer overflows and authentication attacks. Strong authentication methods should be deployed. Software needs to also be carefully developed to meet the quality and security requirements. Software hardening employs various techniques to make the software resilient to cyberattacks. Attacks against the data analytics layer include data manipulation and the alteration of analytical algorithms. Secure transmission and data encryption are needed to secure the data. Data should be validated for correctness. Vulnerability assessments must be regularly performed to secure the system. Users need to better understand the cyber security threats and vulnerabilities and help make the system resilient to cyberattacks.