STRIDE-Based Cybersecurity Threat Modeling, Risk Assessment and Treatment of an In-Vehicle Infotainment System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it