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Record W3005301866 · doi:10.1109/tccn.2020.2971703

A Secure Spectrum Handoff Mechanism in Cognitive Radio Networks

2020· article· en· W3005301866 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.

Bibliographic record

VenueIEEE Transactions on Cognitive Communications and Networking · 2020
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversité du Québec à Montréal
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of Korea
KeywordsComputer scienceCognitive radioEmulationComputer networkHandoverThroughputTransmission (telecommunications)Cognitive networkSecure transmissionAuthentication (law)Mechanism (biology)CognitionComputer securityWirelessTelecommunicationsEncryption

Abstract

fetched live from OpenAlex

The need for more efficient spectrum utilization is becoming more pronounced in our increasingly digitalized society. However, this also introduces a variety of new security threats. In this paper, we introduce a novel cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which can be exploited by intruders during spectrum handoff. Then, we propose a secure handoff mechanism that can successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics. Malicious users can be effectively identified by the coordinating cognitive user by looking up the trust values. The performance of the proposed mechanism is validated using MATLAB simulations. The simulation results show that the utility of the proposed mechanism in terms of its probability in correctly identifying false authentication, detection rate, throughput rate, and transmission delay.

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 categoriesMeta-epidemiology (narrow)
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 score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.264
Teacher spread0.224 · 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