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A Flexible Architecture for Securing Transactions and Data Flows in Smart Home IoT

2025· article· W7117729492 on OpenAlexafffund
Ali Bouchareb, Boucif Amar Bensaber, Ismaïl Biskri

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSoftware deploymentEavesdroppingOverhead (engineering)ArchitectureCryptographic protocolWirelessDatabase transactionInternet of Things

Abstract

fetched live from OpenAlex

The Internet of Things (IoT) is tightening the link between digital systems and the physical world, opening seamless access to real-world assets. Progress in wireless connectivity has accelerated IoT deployment across domains such as surveillance, healthcare, smart homes, urban services, and logistics. This paper targets the cybersecurity risks specific to smart-home settings. We review prominent threats and introduce a flexible architecture for secure transaction handling and data-flow control. Our system combines centralized and decentralized models and integrates application-layer protocols for efficient flow control, minimizing computation in resource-limited devices. At the base layer, Security protocols mitigate vulnerabilities in machine-to-machine (M2M) systems. Experimental results confirm the system's effectiveness in reducing eavesdropping and malware threats while lowering computational overhead for low-energy devices.

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 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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.319
Teacher spread0.271 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes2
Has abstractyes

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