# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### Risk Evaluation and Interpretation  
The TARA methodology employed in this study involved a structured and systematic evaluation of each system asset and its associated threats using the STRIDE framework. This approach ensures a detailed and transparent risk assessment by identifying critical assets within the DMS architecture--such as sensors, actuators, dataflows, and ports--and categorizing potential threats under the six STRIDE threat types. Each threat was further analyzed by defining specific attack paths and documenting assumptions about feasibility. For example, scenarios involving Information Disclosure were linked to attackers gaining access to sensitive dataflows, whereas Tampering scenarios focused on the unauthorized modification of communication data. To ensure consistency and alignment with industry standards, the evaluation relied on detailed impact criteria across four key dimensions:  
* Safety Impact (S): This dimension assessed the threat's potential to cause life-threatening or non-life-threatening injuries. Severe impacts (assigned a value of 10) included scenarios involving fatal injuries, while moderate impacts (4) included partial degradation of system functionality without life-threatening consequences;
* Operational Impact (O): This dimension evaluated the effect on system functionality. Severe operational impacts included complete loss of critical vehicle functions, whereas minor impacts referred to degraded but still functional systems;
* Financial Impact (F): This dimension assessed the potential economic consequences, ranging from negligible financial losses (value of 1) to significant economic costs (value of 10) associated with mitigating or responding to threats;* Privacy and Legislative Impact (P): This dimension addresses the implications of data breaches or violations of regulatory standards, with severe impacts involving highly sensitive data breaches (value of 10). The combined severity of each threat was calculated using a weighted formula to account for the varying importance of each impact dimension:  
\[S_{val}=\frac{5\cdot S+0.5\cdot F+1\cdot O+0.25\cdot P}{4} \tag{2}\]  
The numerical coefficients presented in (2) were derived based on a rational evaluation of the relative importance of each impact dimension within the context of the DMS. The coefficient assigned to Safety Impact (S) is set to 5, reflecting its paramount significance in the context of automotive systems, where safety is the primary consideration. This coefficient ensures that the formula heavily weights the potential for life-threatening consequences. The coefficient for Operational Impact (O) is 1, indicating its secondary importance compared to safety. Although operational disruptions can affect the functionality of the system, they generally do not pose direct risks to human life. The moderate weighting reflects the need to maintain the operability of the system without overshadowing safety concerns. The coefficient for Financial Impact (F) is set at 0.5, reflecting the lower priority assigned to economic loss compared to safety and operational functionality. In the context of automotive safety, financial considerations, while relevant, do not take precedence over protecting human lives and maintaining critical functions. The coefficient for Privacy and Legislative Impact (P) is 0.25, representing the lowest priority among the considered factors. Although data privacy breaches and legislative violations are significant, they do not directly compromise immediate physical safety or system functionality. This minimal weight prevents these factors from disproportionately influencing the overall severity score. Thus, the weighted formula is carefully structured to prioritize human safety while adequately considering other factors in a balanced manner. The division by 4 ensures normalization, keeping the severity values within a consistent and interpretable range.