# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 7 Results and Discussion  
Implementing a DMS represents a significant advancement in automotive safety, enabling real-time monitoring of driver behavior to detect signs of fatigue and distraction. As these systems evolve, they also introduce considerable cybersecurity challenges. This study employs a comprehensive TARA methodology, in accordance with ISO/SAE 21434, to systematically identify and categorize security threats. A total of 115 threats were classified as follows:  
* 95 low-risk threats;
* 16 medium-risk threats;
* 4 high-risk threats. The majority of low-risk threats are attributed to the localized architecture of the DMS, which limits external communication and reduces the system's attack surface. However, several high- and medium-risk threats emerged, particularly in unauthorized sensor manipulation, data Spoofing, and Denial of Service attacks, necessitating multi-layered mitigation strategies. **Component risk profiles and residual risks. ** Each key component was analyzed for residual risk after primary mitigations were applied:  
* **Calculation Unit (CU):** High initial risk from Spoofing, Tampering, DoS, and Elevation of Privilege, mitigated through MACs, encryption, IDS/IPS, and access controls. Residual risks stem from key management complexities, real-time performance constraints, and susceptibility to zero-day exploits;
* **Camera Activator (CA):** Exposed to Spoofing and Elevation of Privilege; mitigated via TLS/SSL, AAA frameworks, and digital signatures. However, certificate management latency and the integration burden of AUTOSAR remain unresolved;
* **Waming devices (WD):** Moderate initial risks managed by access control and redundancy. Still, they face scalability challenges and anomaly detection evasion scenarios;
* **Vehicle Status Analysis (VSA):** Subject to Spoofing and Tampering; secured through encryption, tamper-evident hardware, and role-based access controls. Risks persist in maintenance overhead and ensuring cryptographic agility;
* **Camera sensors (CS):** Exposed to Spoofing, Repudiation, and Information Disclosure; mitigated via digital signatures, encryption, and anomaly detection. Nevertheless, physical exposure and key lifecycle issues remain significant. **Overall Risk Reduction Assessment. ** Table 4 summarizes the assessed risk reduction achieved for the most critical threats identified across key system components, illustrating the effectiveness of applying the primary security controls discussed throughout Section 6.3. As the table indicates, implementing standard mitigations like authentication (MACs), encryption, access controls (AAA), and baseline IDS/IPS significantly lowers the initial risk severities. For example, threats initially assessed as High are generally reduced to Medium, and Medium threats often decrease to Low. However, the table also highlights that notable residual risks persist, particularly for components like the Calculation Unit (CU) and camera sensor (CS) under specific threat categories (Spoofing, Tampering, DoS, and Elevation of Privilege). The persistence of Medium residual risks even after applying primary controls stems directly from the inherent limitations of these mechanisms in the automotive context. For instance, while authentication hinders basic Spoofing (S) for the CU, the Medium residual risk reflects challenges in perfectly secure key management for MACs, the difficulty of implementing complex authentication protocols without performance overhead in embedded systems, and the potential for sophisticated attacks designed to bypass standard verification methods. Similarly, the Medium residual risk for Tampering (T) on the CU, despite encryption and IDS/IPS, acknowledges the complexity of tuning detection systems effectively against novel threats without causing false positives, the unavoidable performance overhead, and the possibility of attacks targeting implementation flaws rather than the cryptographic algorithms themselves.