# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### STRIDE Application to System Components
#### Calculation Unit  
The Calculation Unit, acting as the central processor of data from sensors and the issuer of commands to actuators, is a pivotal component of the DMS. Due to its role as a central hub, it is a primary target for various threats. **Spoofing** attacks pose a significant risk, allowing adversaries to impersonate the Calculation Unit and inject falsified data into the system, potentially leading to operational disruptions and end \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**STRIDE** & **CU** & **CS** & **CA** & **WD1** & **WD2** & **Other ECUs** & **VSA** & **SBS** & **SPS** \\ \hline S & & & & & & & & & \\ T & & & & & & & & & \\ R & & & & & & & & & \\ I & & & & & & & & & \\ D & & & & & & & & & \\ E & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: STRIDE safety impact table. pering** with the Calculation Unit's data can compromise privacy and disrupt functionality, involving unauthorized modifications to sensitive information or the insertion of malicious commands. **Repudiation** risks arise when malicious actions by the Calculation Unit cannot be traced or verified, hindering accountability. **Information Disclosure** is particularly relevant due to the processing of sensitive driver data (biometric and behavioral), where unauthorized access constitutes a privacy breach. **DoS** attacks threaten the availability of the Calculation Unit, potentially rendering it unresponsive under a flood of malicious requests. Finally, **Elevation of Privilege** attacks exploit vulnerabilities to gain unauthorized access to critical functions, potentially enabling attackers to manipulate system behaviors. To mitigate these risks, several security measures are considered. Authentication mechanisms, such as message authentication codes (MACs), are effective against **Spoofing** by ensuring communication authenticity. Robust encryption and intrusion detection/prevention systems (Intrusion Detection System (IDS)/Intrusion Prevention System (IPS)) address **Tampering** by protecting data integrity and detecting unauthorized access; these IDS/IPS systems could be further enhanced in future implementations by incorporating artificial intelligence (AI) or machine learning (ML) models for adaptive anomaly detection based on learned normal system behavior. Cryptographic digital signatures mitigate **Repudiation** risks by binding actions to a verifiable identity, and exploring decentralized ledger technologies like blockchain could offer enhanced, immutable logging capabilities for critical operations, further bolstering non-Repudiation. Encryption techniques (Advanced Encryption Standard (AES) and key encapsulation mechanism (KEM)/Data Encapsulation Mechanism (DEM)) [32] are necessary to prevent **Information Disclosure**. Rate-limiting mechanisms and anomaly detection through next-generation firewalls effectively address **DoS** risks by filtering abnormal traffic, although AI-based approaches could potentially improve the accuracy of distinguishing attacks from legitimate peaks. Comprehensive access control frameworks, like Authentication, Authorization, and Accounting (AAA), enforce strict role-based permissions to counter **Elevation of Privilege** threats [33]. While these proposed mitigations provide a strong baseline, they may not address all practical challenges, leading to residual risks. Balancing security with computational efficiency for authentication (**Spoofing**) is critical in embedded systems. Encryption and IDS/IPS (**Tampering**), especially AI-enhanced versions, introduce complexity and potential performance overheads. Digital signatures and potential blockchain implementations (**Repudiation**) depend on secure key management and robust consensus mechanisms, respectively, with tamper-proofing in automotive environments remaining an open question. Secure key distribution for encryption (**Information Disclosure**) is challenging in interconnected vehicles.