Evaluation of Wireless Deauthentication Attacks and Countermeasures on Autonomous Vehicles
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
An autonomous vehicle (AV), also known as driver-less car, is able to operate itself and execute critical activities without the need for human interaction. Sensors, complicated algorithms, machine learning systems and powerful processors are used to run software in autonomous vehicle. The majority of modern car models in the market has integrated Wi-Fi modules which are used to send telemetry data to back-end cloud servers and obtain real-time traffic data. In this paper, we will demonstrate how deauthentication attacks can be successfully executed on Wi-Fi attack surface of an autonomous vehicle. This is accomplished by creating different attack scenarios and performing attack analysis. We analyze mitigation strategies such as enabling Management Frame Protection (MFP) in Wi-Fi Protected Access 2 (WPA2) and using Wi-Fi Protected Access 3 (WPA3) to protect against deauthentication attacks. Based on our analysis, using WPA3 is the best mitigation technique against deauthentication attacks in AVs.
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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 | 0.001 | 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 | 0.000 |
| 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