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Record W2740609710

Analysis of Covert Hardware Attacks

2014· article· en· W2740609710 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
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceComputer securityCovert channelSide channel attackHardware security moduleCovertCryptographySchema (genetic algorithms)Embedded systemComputer hardwareCloud computing securitySecurity information and event managementOperating systemCloud computing
DOInot available

Abstract

fetched live from OpenAlex

Current embedded system, such as cell phones and smart-cards, in corporate security devices or cryptographic processor. These cryptographic devices often store private keys or other sensitive data, so compromise of this data or the underlying hardware may lead to loss of privacy, forged access, or monetary theft. Even if the attackers fail to gain the secret information that is stored in a hardware, they may be able to disrupt the hardware or deny service leading to other kinds of security failures in the system. Therefore hardware attacks targets this security devices. Hardware attacks could be covert or overt based on awareness of the targeted system. This paper reviews proposed Accessibility/Resources/Time (ART) schema that quantifies hardware attacks. We focus in this paper on presenting covert attacks and quantify the attack using the ART schema. Keywords-hardware attack; side-channel attack; ART schema; hardware security; covert attack;

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: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.010
GPT teacher head0.232
Teacher spread0.223 · 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