MétaCan
Menu
Back to cohort
Record W2035650567 · doi:10.1002/sec.35

Insider attack attribution using signal strength‐based hyperbolic location estimation

2008· article· en· W2035650567 on OpenAlexafffund
Christine Laurendeau, Michel Barbeau

Bibliographic record

VenueSecurity and Communication Networks · 2008
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceTransmission (telecommunications)RSSComputer securityTransmitterProbabilistic logicSIGNAL (programming language)Verifiable secret sharingComputer networkAlgorithmTelecommunicationsChannel (broadcasting)Set (abstract data type)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract A rogue insider, in a wireless network, is an authenticated member that exploits possession of a valid identity in order to launch an attack. A typical example is the transmission of a verifiable message containing false or incomplete information. An important step, in enabling the network authorities to attribute an attack message to its originator, involves locating the physical source of the transmission. We propose a probabilistic scheme to determine the location of a transmitting rogue, with a degree of confidence, using the relative signal strength received by neighboring devices, even if the effective isotropic radiated power (EIRP) employed by the rogue is unknown. The relative received signal strength (RSS) between pairs of trusted receivers are combined with a range of possible EIRP values to construct an area in Euclidian space bounded by minimum and maximum distance hyperbolas. The area contained within the intersection of multiple hyperbola pairs pinpoints the location of the rogue transmitter with a specific level of confidence. Copyright © 2008 John Wiley & Sons, Ltd.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.034
GPT teacher head0.258
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueSecurity and Communication NetworksSame topicWireless Communication Security TechniquesFrench-language works237,207