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Record W2808669017 · doi:10.1109/tdsc.2018.2846258

Physical Layer based Message Authentication with Secure Channel Codes

2018· article· en· W2808669017 on OpenAlexafffund
Dajiang Chen, Ning Zhang, Nan Cheng, Kuan Zhang, Zhiguang Qin, Xuemin Shen

Bibliographic record

VenueIEEE Transactions on Dependable and Secure Computing · 2018
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Postdoctoral Science Foundation
KeywordsComputer scienceAuthentication (law)Message authentication codeAuthentication protocolComputer networkPhysical layerData Authentication AlgorithmPHYHash functionChannel (broadcasting)Theoretical computer scienceComputer securityCryptographyWireless

Abstract

fetched live from OpenAlex

In this paper, we investigate physical (PHY) layer message authentication to combat adversaries with infinite computational capacity. Specifically, a PHY-layer authentication framework over a wiretap channel (W1; W2) is proposed to achieve information theoretic security with the same key. We develop a theorem to reveal the requirements/conditions for the authentication framework to be information-theoretic secure for authenticating a polynomial number of messages in terms of n. Based on this theorem, we design an authentication protocol that can guarantee the security requirements, and prove its authentication rate can approach infinity when n goes to infinity. Furthermore, we design and implement a feasible and efficient message authentication protocol over binary symmetric wiretap channel (BSWC) by using Linear Feedback Shifting Register based (LFSR-based) hash functions and strong secure polar code. Through extensive simulations, it is demonstrated that the proposed protocol can achieve high authentication rate, with low time cost and authentication error rate.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations56
Published2018
Admission routes2
Has abstractyes

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Same venueIEEE Transactions on Dependable and Secure ComputingSame topicWireless Communication Security TechniquesFrench-language works237,207