# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 8 Conclusions  
This study successfully addressed the research questions posed in Section 3.2, offering a comprehensive assessment of the cybersecurity posture of a representative DMS. The analysis confirmed that the system's architecture--built upon localized communication interfaces such as GPIO ports and CAN buses--significantly reduces exposure to external threats. Nevertheless, the Threat Analysis and Risk Assessment (TARA) identified 115 threats, with a small but notable subset (16 medium-risk and 4 high-risk) requiring targeted mitigation strategies. \begin{table}
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Through the STRIDE-based evaluation, we demonstrated that while conventional security controls such as MACs, access controls (AAA), encryption, and IDS/IPS effectively reduce most risks to acceptable levels, some residual vulnerabilities remain. These are largely due to implementation constraints in embedded automotive environments--particularly in secure key management, real-time processing limitations, and the dynamic nature of vehicular ecosystems. In response to emerging challenges and reviewer feedback, we explored the integration of advanced countermeasures, including adaptive AI-based intrusion detection systems, blockchain-based key management and logging frameworks, and edge-computing techniques for localized anomaly detection. We argue that these technologies are well-suited to further lower residual risks and bolster trust and verifiability, particularly in more connected and cooperative environments like Vehicle-to-Everything (V2X) networks. Moreover, this study underscores the importance of addressing legal and ethical dimensions--most notably, compliance with General Data Protection Regulation (GDPR). By leveraging edge analytics, anonymized data indexing, and user-centric consent mechanisms, DMS architectures can support both functional safety and data sovereignty. To institutionalize privacy-by-design principles, we recommend the adoption of integrated assessments such as the Data and Impact Privacy Integrated Assessment (DIPIA), which merges security modeling with legal accountability frameworks. In conclusion, the analyzed DMS architecture exhibits a strong foundational security posture but must evolve in parallel with technological, regulatory, and societal expectations. Future developments should prioritize the following:  
* Deployment of adaptive threat detection via AI-enhanced IDS;
* Integration with V2X infrastructures to facilitate distributed anomaly awareness;
* Application of blockchain for decentralized key and log management;
* Embedding of DIPIA-driven compliance practices throughout the design lifecycle. By addressing these multidimensional considerations, future DMS implementations can achieve greater resilience, regulatory alignment, and public trust in increasingly autonomous and data-driven vehicular environments. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15105571/s1](https://www.mdpi.com/article/10.3390/app15105571/s1)]([https://www.mdpi.com/article/10.3390/app15105571/s1](https://www.mdpi.com/article/10.3390/app15105571/s1)), The detailed TARA analysis. Conceptualization, R.K.; data curation, E.J. ; investigation, I-C.S. ; methodology, M.D.S. ; software, E.J. ; supervision, C.E. ; validation, R.K.; writing--original draft, M.D.S., E.J., and I-C.S. ; writing--review and editing, M.D.S. All authors have read and agreed to the published version of the manuscript. The research work was completed by the on-site participants of the 3 rd version of Automotive Cybersecurity Academy (ACSA) 2024, which is funded by the European Commission as part of Erasmus+ Blended Intensive Program (BIP). More information about this edition of mobility can be found at the following address: [[https://www.autocybersec.ro/all-acasas/acsa-2024](https://www.autocybersec.ro/all-acasas/acsa-2024)]([https://www.autocybersec.ro/all-acasas/acsa-2024](https://www.autocybersec.ro/all-acasas/acsa-2024)) (accessed on 12 May 2025).