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Record W1979697391 · doi:10.1109/tla.2012.6142473

Analysis of Relay Attacks on RFiD Systems

2012· article· en· W1979697391 on OpenAlexaff
José Lima, Ali Miri, Monica Nevins

Bibliographic record

VenueIEEE Latin America Transactions · 2012
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of OttawaToronto Metropolitan UniversitySTMicroelectronics (Canada)
Fundersnot available
KeywordsRelayComputer securityComputer scienceOrthogonalityInterference (communication)Identification (biology)CryptographyVariety (cybernetics)Computer networkClass (philosophy)Radio-frequency identificationTelecommunicationsChannel (broadcasting)Mathematics

Abstract

fetched live from OpenAlex

Today Radio Frequency identification RFiD systems are widely used in a variety of security sensitive applications such as access control, the payment industry and many others. An important class of attacks on these types of systems is that of relay attacks, due to the orthogonality of such attacks to the existing security and cryptographic solutions. In this paper we address the open question of the maximum distance between the rogue Reader and the victim TAG under which a relay attack can be successful. We determine that interference is the largest confounding factor, but that with proper interference cancelation, a distance of up to 10m is possible. This paper provides an analysis of the maximum distance for successful communication between the rogue Reader and the legitimate TAG,herein called the victim distance. We also propose several countermeasures to the relay attack.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.248
Teacher spread0.233 · 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 designObservational
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

Citations2
Published2012
Admission routes1
Has abstractyes

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