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Record W4408310766 · doi:10.1002/spy2.70016

IoT and Man‐in‐the‐Middle Attacks

2025· article· en· W4408310766 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

VenueSecurity and Privacy · 2025
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsPolytechnique MontréalUniversité de Montréal
Fundersnot available
KeywordsComputer securityComputer scienceHistory

Abstract

fetched live from OpenAlex

ABSTRACT This article provides an overview of the Internet of things (IoT) and its growing significance in today's interconnected world. It discusses the concept of man‐in‐the‐middle (MitM) attacks in detail, including their various types, causes, and potential impacts on IoT networks. The article analyzes MitM attacks at different layers of the IoT architecture and explores current prevention techniques and mitigation strategies. It addresses the challenges in detecting and preventing such attacks, particularly in the context of the heterogeneous and resource‐constrained nature of IoT devices. The article also examines emerging technologies, such as machine learning and blockchain, for enhancing IoT security. Furthermore, it discusses open issues, including the challenges of prevention, the potential impact of new technologies, and future trends in MitM attacks. By exploring these aspects, the article aims to provide insights into improving detection and prevention mechanisms against MitM attacks in IoT environments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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