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Record W4253753479 · doi:10.1145/2786763.2694380

Protecting Data on Smartphones and Tablets from Memory Attacks

2015· article· en· W4253753479 on OpenAlexaff
Patrick Colp, Jiawen Zhang, James P. Gleeson, Sahil Suneja, Eyal de Lara, Himanshu Raj, Stefan Saroiu, Alec Wolman

Bibliographic record

VenueACM SIGARCH Computer Architecture News · 2015
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceEmbedded systemDramComputer hardwareCode (set theory)Class (philosophy)Memory protectionMobile deviceOperating systemComputer securityComputer data storageRegistered memoryExtended memory

Abstract

fetched live from OpenAlex

Smartphones and tablets are easily lost or stolen. This makes them susceptible to an inexpensive class of memory attacks, such as cold-boot attacks, using a bus monitor to observe the memory bus, and DMA attacks. This paper describes Sentry, a system that allows applications and OS components to store their code and data on the System-on-Chip (SoC) rather than in DRAM. We use ARM-specific mechanisms originally designed for embedded systems, but still present in today's mobile devices, to protect applications and OS subsystems from memory 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.093
GPT teacher head0.304
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

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