Prevention of management frame attacks on 802.11 WLANs
Bibliographic record
Abstract
Since the ratification of the IEEE 802.11 standard, 802.11 Wireless LANs (WLANs) have been widely deployed in research, government, military and industrial environments. However, 802.11 WLANs suffer from a number of security problems. In particular, management frames in 802.11 WLANs are not protected. A number of attacks such as denial of service, impersonation and man-in-the-middle can be launched by exploiting unprotected management frames. Even the newly ratified 802.11i security standard does not protect the network against such attacks. We present a per-frame authentication scheme to protect 802.11 management frames. With this scheme, every frame received by the wireless client or access point is first authenticated and then the corresponding management function carried out. Our scheme is compatible with the original 802.11 standard and uses the most of the 802.11 standard resources. We have implemented a prototype of our scheme and built a test bed to launch management frame attacks and to demonstrate how our scheme can prevent such 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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".