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Record W2784107617 · doi:10.1109/uemcon.2017.8248990

Electromagnetic analysis method for ultra low power cipher Midori

2017· article· en· W2784107617 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceEgg Farmers of CanadaNational Security Agency
KeywordsSide channel attackCipherCryptographyComputer sciencePower analysisPower (physics)Power consumptionElectromagnetic radiationKey (lock)Electrical engineeringComputer securityEngineeringEncryptionPhysicsOptics

Abstract

fetched live from OpenAlex

Recently, low power ciphers that can be used in IoT devices have attracted the attention of many researchers. The power consumption of Midori is the lowest of all available ciphers. In hardware security, side-channel attacks pose a danger because they illegally analyze the secret key in a cryptographic device using the power consumption and electromagnetic waves generated during the device's operation. One type of side-channel attack that uses electromagnetic waves is called electromagnetic analysis. To examine the safety of future IoT devices, it is extremely important to investigate the resistance of Midori, an extremely low-power cipher, to electromagnetic analysis (tamper resistance). However, to our knowledge, no studies have reported on electromagnetic analysis against Midori. The present study proposes a method of electromagnetic analysis.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.470

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 science0.0010.000
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 head0.016
GPT teacher head0.340
Teacher spread0.324 · 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