Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System
Bibliographic record
Abstract
Passive keyless entry and start system has been widely used in modern cars. Car owners can open the door or start the engine merely by having the key in their pocket. PKES was originally designed to establish a communication channel between the car and its key within approximately one meter. However, the channel is vulnerable to relay attacks by which attackers unlock the door even if the key is out of range. Even though relay attacks have been recognized as a potential threat for over ten years, such attacks were thought to be impractical due to highly expensive equipment; however, the required cost is gradually practical. Recently, a relay attack has been demonstrated with equipment being sold only under $100. In this paper, we propose a sound-based proximity-detection method to prevent relay attacks on PKES systems. The sound is eligible to be applied to PKES because audio systems are commonly available in cars. We evaluate our method, considering environments where cars are commonly parked, and present the recording time satisfying both usability and security. In addition, we newly define an advanced attack, called the record-and-playback attack, for sound-based proximity detection, demonstrating that our method is robust to such an attack.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".