Exploiting Race Condition for Wi-Fi Denial of Service Attacks
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
Wi-Fi is a wireless communication technology that has been around since the late nineties. Nowadays, it is the most adopted wireless technology in various IoT (Internet of Things) applications. Although Wi-Fi security has significantly improved throughout the past years, it is still lagging behind. Many vulnerabilities exist allowing attackers to generate different types of attacks. These attacks can breach the authentication, confidentiality, and data integrity of Wi-Fi networks. In terms of attack impact, attacks on availability have a higher impact. In fact, breaching nowadays systems does not only result in data corruption but may also result in the loss of human lives. Therefore, more consideration should be brought to attacks on availability. In this paper, we present three attacks on Wi-Fi availability. These attacks cause a denial of service on Wi-Fi users by preventing them from connecting to a legitimate network. We adopt the evil twin scheme and exploit a race condition-based vulnerability to generate the attacks. Also, we propose countermeasures to fix the exploited vulnerability and mitigate the attacks.
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