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Write Blocker for Internet of Things Flash Technologies

2023· article· en· W4388894030 on OpenAlex
Matthew Roffel, Xiaodong Lin

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDigital and Cyber Forensics
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceBlocking (statistics)StandardizationNISTFlash (photography)Reliability (semiconductor)Internet of ThingsThe InternetSoftwareEmbedded systemBlock (permutation group theory)Computer securityComputer networkWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

Write Blockers are an important tool preserving digital evidence integrity and protecting the data chain of custody during a digital forensics investigation, which is crucial to the admissibility and reliability of evidence in court. One area in which write blocking tools are lacking is the Internet of Things (IoT) space. There are unique challenges to the IoT storage technologies and write-blocking them, mainly the lack of standardization in the IoT space. To address it, in this paper, we propose the design of novel write blocking tools for IoT flash technologies. We first develop a set of requirements inspired by the existing requirements for hard drive write blockers as defined by the National Institute of Standards and Technology (NIST). Afterwards, we implement a Serial Peripheral Interace (SPI) flash write blocker in both hardware and software. Furthermore, a demonstration is presented to show effectiveness of the proposed write blocking systems, and the future work is proposed.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.156

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 head0.226
Teacher spread0.210 · 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

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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