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Record W2946207961 · doi:10.23919/date.2019.8715150

Methodology for EM Fault Injection: Charge-based Fault Model

2019· article· en· W2946207961 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFault injectionComputer scienceFault modelFault (geology)Key (lock)Embedded systemFault coverageSet (abstract data type)Stuck-at faultReliability engineeringSoftwareFault detection and isolationElectrical engineeringComputer securityEngineeringOperating systemElectronic circuitProgramming language

Abstract

fetched live from OpenAlex

Recently electromagnetic fault injection (EMFI) techniques have been found to have significant implications on the security of embedded devices. Unfortunately there is still a lack of understanding of EM fault models and countermeasures for embedded processors. For the first time, this paper proposes an extended fault model based on the concept of critical charge and a new EMFI backside methodology based on over-clocking. Results show that exact timing of EM pulses can provide reliable repeatable instruction replacement faults for specific programs. An attack on AES is demonstrated showing that the EM fault injection requires on average less than 222 EM pulses and 5.3 plaintexts to retrieve the full AES key. This research is critical for ensuring embedded processors and their instruction set architectures are secure and resistant to fault injection attacks.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.963
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.088
GPT teacher head0.352
Teacher spread0.264 · 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