Harnessing Fuzzing Capabilities to Improve Automotive Cybersecurity
Bibliographic record
Abstract
The automotive industry’s rapid adoption of electric vehicles (EVs) and advanced networking introduces new avenues for performance enhancement but also creates significant cybersecurity risks. As EVs integrate complex cyber-physical systems, ensuring their safety and reliable operation especially within the context of Connected and Autonomous Vehicles (CAVs) is critical, given the substantial threat posed by cyberattacks to both vehicle infrastructure and power grids. A single compromised Electronic Control Unit (ECU) or infected vehicular network could cascade into a fleet-wide security risk. This paper performs a comprehensive vulnerability analysis to evaluate the cyber risks associated with various modern vehicle modules and assesses their potential risk impact levels, detailing several common attack pathways. We also discuss security testing methods, including penetration testing and dynamic analysis, and highlight the critical role and potential of fuzzing in automotive cybersecurity. Furthermore, we conduct a detailed comparative analysis of existing fuzzers, assessing their current suitability for automotive industrial use. Finally, we propose a novel fuzzing-based security framework that specifically integrates and addresses automotive security concerns by evaluating the security performance against the fundamental pillars of Confidentiality, Integrity, and Availability, ensuring reliability and safety of vehicle systems.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".