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Record W3171733056 · doi:10.1109/tii.2021.3089462

Man-in-the-Middle Attack Mitigation in Internet of Medical Things

2021· article· en· W3171733056 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 Industrial Informatics · 2021
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMan-in-the-middle attackComputer securityComputer scienceInternet of ThingsALARMAuthentication (law)Replay attackKey (lock)Data transmissionThe InternetTransmission (telecommunications)Real-time computingComputer networkEngineeringTelecommunicationsWorld Wide Web

Abstract

fetched live from OpenAlex

The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.383

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.092
GPT teacher head0.289
Teacher spread0.197 · 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