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Record W1984879065 · doi:10.1504/ijwmc.2009.028895

Prevention of management frame attacks on 802.11 WLANs

2009· article· en· W1984879065 on OpenAlex
Wenfeng Ge, Jing Li, Srinivas Sampalli

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Wireless and Mobile Computing · 2009
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceComputer securityDenial-of-service attackComputer networkFrame (networking)Scheme (mathematics)IEEE 802.11e-2005Authentication (law)Wireless networkTelecommunicationsWireless

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.306
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it