## 3 Results
### Agriculture Case Study  
Figure 2 shows the application of THREATGET and threat modeling on the basis of an agricultural example. The diagram contains a digital twin of automated agricultural components on a generic basis. From bottom to top, the field devices represent components located directly at the fields. These are sensors that conduct humidity, temperature, or PH measurements; or actuators that conduct actions based on these measurements or on a regular basis, such as watering the plants or applying nutrients. Please note that this  
diagram represents an abstraction of a real-world system on a generic basis. Therefore, components such as sensors or actuators are only modeled once, since the detected threats would be the same for multiple sensors from a cybersecurity perspective. Specific types of sensors or actuators are out of scope for the presented example. The sensor fusion takes care of correlating measurement data, validating the received data and detecting and correcting discrepancies between different sensors. Once processing at the sensor fusion component has finished, data are forwarded to an edge computing unit, which is a computational device located close to the area of application, which is considered the central processing unit of the whole system. It coordinates multiple field-level components and triggers higher-level actions based on the sensor data provided. These actions are forwarded to the actuator ECU, which then controls the actuators to perform their intended operations. The communication devices, such as the field gateway, serve the purpose of receiving and sensing information from/to that edge computing unit and connect it to a cloud storage for data acquisition, learning, and optimization for the future. Finally, the external environment represents the external interaction with our proposed agriculture case study. This section illustrates that the external interactor communicates with our architecture through two primary paths: cloud storage (i.e., **Path 1**) and sensor (i.e., **Path 2**) data flow. Consequently, we believe that any attack propagation within the system network originates from one of these two paths. This example is based on the ThreatGet tool, and it includes all of the security attributes required for each system component to build protective security mechanisms for mitigating cyber risks. The results of the tool are introduced in the following section in terms of a set of potential threats, affected elements, and associated security attributes described as system vulnerabilities. Risk Analysis Outcomes  
THREATGET is utilized to identify existing security vulnerabilities that yield negative consequences to the system under consideration. As described in Section 2.4.5, each system component has security attributes that reflect the protection against multiple attack scenarios, such as authentication, authorization, tamper protection, and encryption. In order to provide a set of risk mitigation actions against potential threats, the system architect can specify these characteristics [16]. Therefore, THREATGET checks for violations of these characteristics and indicates if there is a security vulnerability that could allow malicious activity to take place the system model shown in Figure 5.  
Based on THREATGET's database discussed in Section 2.4.2, the tool deeply investigated all known security issues. The tool identified 83 threats which were classified into six main categories based on the STRIDE methodology, as discussed in Section 2.4.3. Figure 6 illustrates the classification of all identified threats according to the STRIDE model. The chart explains that 20 threats were categorized as tampering, whilst 18 threats were classified as information disclosure. The spoofing category was considered to have the highest rate of all identified threats, with 25 threats classified in this category. The repudiation and elevation of privilege categories were considered to have the lowest rates, with only three and two threats classified into each category, respectively.