MétaCan
Menu
Back to cohort
Record W4312896866 · doi:10.1109/tnano.2022.3214341

Deep Exploration on Fault Model of Electromagnetic Pulse Attack

2022· article· en· W4312896866 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 Nanotechnology · 2022
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsTrinity College
FundersNational Natural Science Foundation of China
KeywordsEMPAFault (geology)Electronic engineeringEngineeringWaveformDigital electronicsElectronic circuitComputer scienceElectrical engineeringMaterials science

Abstract

fetched live from OpenAlex

The efficient fault injection attack (FIA) technique, electromagnetic pulse attack (EMPA), becomes a severe threat to the security of integrated circuits (ICs). Understanding the fault model of EMPA is necessary to protect ICs against EMPA. This work investigates the fault model of EMPA on digital circuits in depth by exploring its fault behaviors, fault conditions and fault causes. During exploration, a new kind of sampling fault model, called S-sampling fault model, is found. By adding the new finding, the fault models of EMPA can be built, fully covering the combinational and sequential digital circuits, the positive and negative polarity of EM pulse, and the signals processed by the circuits. The investigation is carried out based on the circuit-level simulation, considering the disturbances on the IC power and ground grids caused by EMPA. The insights into the EMPA fault models allow circuit designers to design more efficient countermeasures against EMPA.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.236
Teacher spread0.213 · 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