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Record W4311300305 · doi:10.54097/fcis.v2i1.3162

A Review of Security Research on the Internet of Things, Based on Artificial Intelligence and Blockchain

2022· review· en· W4311300305 on OpenAlex
Ni Zhang

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

VenueFrontiers in Computing and Intelligent Systems · 2022
Typereview
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBlockchainInternet of ThingsComputer scienceComputer securityKey (lock)Intrusion detection systemCryptographyThe InternetWorld Wide Web

Abstract

fetched live from OpenAlex

With the rapid improvement of digital technology, the Internet of things (IoT) has become a trending development direction. Its massive data interaction capabilities have drawn researchers’ attention to key security issues. This paper describes the concept of IoT, its application areas, and corresponding security problems. The use of blockchain and cryptographic algorithms is introduced, and the application of blockchain in IoT security is analyzed and discussed in detail. Drawing upon artificial intelligence, technical solutions such as using machine learning for privacy protection and intrusion detection are presented. Finally, the problems and challenges facing IoT, driven by blockchain and artificial intelligence, are discussed.

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)0.008
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.002
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 head0.105
GPT teacher head0.357
Teacher spread0.251 · 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