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Record W2012166391 · doi:10.1109/cjece.2007.364330

Wireless security through RF fingerprinting

2007· article· en· W2012166391 on OpenAlex

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsCommunications Research Centre Canada
FundersIndustry Canada
KeywordsFingerprint (computing)Computer scienceWirelessWireless networkNode (physics)Computer networkIdentification (biology)Fingerprint recognitionRadio frequencyComputer securityEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The process of identifying radio transmitters by examining their unique transient characteristics at the beginning of transmission is called RF fingerprinting. The security of wireless networks can be enhanced by challenging a user to prove its identity if the fingerprint of a network device is unidentified or deemed to be a threat. This paper addresses the problem of identifying an individual node in a wireless network by means of its RF fingerprint. A complete identification system is presented, including data acquisition, transient detection, RF fingerprint extraction, and classification subsystems. The classification performance of the proposed system has been evaluated from experimental data. It is demonstrated that the RF fingerprinting technique can be used as an additional tool to enhance the security of wireless networks.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.412

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 science0.0000.000
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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