# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 3 Research Methodology
### Selection of Automotive System  
The initial phase involved identifying a relevant automotive system to focus the cybersecurity investigation. Due to the broad scope of modern vehicle subsystems, a comparative evaluation was carried out across five categories: Advanced Driver Assistance Systems (ADASs), infotainment systems, DMSs, predictive maintenance, and automatic emergency braking. These categories were assessed based on their technological maturity, adoption trends in the automotive industry, and the relevance of cybersecurity concerns, as outlined by ISO/SAE 21434 [6]. While all considered systems present important security challenges, DMSs were selected as the target of this study due to their increasing integration in modern vehicles, their reliance on biometric data processing, and the critical implications of cybersecurity breaches on driver safety and privacy. Among the candidate domains, DMSs offer a particularly rich attack surface, including real-time sensor input, AI-based decision modules, and in-vehicle communication protocols, all of which make them ideal for a structured threat modeling and risk assessment exercise. To support the architectural and threat modeling analysis, a structured literature review was conducted to identify relevant studies on DMSs published between 2020 and 2024. The goal was to extract architectural models and implementation details from credible sources, ensuring a comprehensive understanding of current design practices and security concerns. The selection and data extraction process is summarized in Figure 2. The process began with the identification of primary studies through manual searches in key scientific databases, including Google Scholar, IEEE Xplore, ACM Digital Library, and ScienceDirect. The search targeted peer-reviewed publications within the fields of automotive systems, cybersecurity, and artificial intelligence. A total of 30 publications were retrieved across these databases: A total of 10 from Google Scholar, 14 from IEEE Xplore, 3 from ACM, and 3 from ScienceDirect. Following the initial search, a relevance screening was conducted based on title and abstract review. Studies were retained if they were published after 2020, addressed the design or implementation of DMSs, and related specifically to automotive systems. Publications that did not meet these criteria were excluded from further consideration. After this stage, 25 studies remained eligible for full-text analysis. Each of the 25 remaining studies was then reviewed in detail to assess methodological rigor and technical relevance. The inclusion criteria at this stage focused on the presence of architectural schematics, component-level descriptions, and discussions of dataflow or system integration. Studies that lacked such content, or that were limited to general surveys without in-depth analysis of specific architectures, were excluded. Ultimately, 21 publications satisfied all conditions and were included in the final corpus for data extraction. Key information concerning component configurations, system workflows, communication protocols, sensor integration, and existing cybersecurity mechanisms was extracted from these studies. This knowledge base informed the selection of a representative DMS architecture and provided input for the subsequent threat modeling and risk assessment phases.