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Record W2559919425 · doi:10.1145/2991079.2991085

Formal security analysis of smart embedded systems

2016· article· en· W2559919425 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRewritingInternet of ThingsEmbedded systemComputer securityModel checkingSet (abstract data type)Smart meterState (computer science)Formal methodsFormal verificationSmart gridSoftware engineeringProgramming language

Abstract

fetched live from OpenAlex

Smart embedded systems are core components of Internet of Things (IoT). Many vulnerabilities and attacks have been discovered against different classes of IoT devices. Therefore, developing a systematic mechanism to analyze the security of smart embedded systems will help developers discover new attacks, and improve the design and implementation of the system. In this paper, we formally model the functionalitiy of smart meters, as an example of a widely used smart embedded device, using rewriting logic. We also define a formal set of actions for attackers. Our formal model enables us to automatically analyze the system, and using model-checking, find all the sequences of attacker actions that transition the system to any undesirable state. We evaluate the analysis results of our model on a real smart meter, and find that a sizeable set of the attacks found by the model can be applied to the smart meter, using only inexpensive, commodity off-the-shelf hardware.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.257
Teacher spread0.237 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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