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Record W2964372587 · doi:10.1109/ccaa.2018.8777580

3-Dimensional Analysis of Cyber-physical Systems Attacks

2018· article· en· W2964372587 on OpenAlexaff
Mridula Sharma, Fayez Gebali, Haytham Elmiligi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceCyber-physical systemWireless sensor networkCyberspaceComputer securityTaxonomy (biology)Scheme (mathematics)Node (physics)Computer networkThe InternetEngineeringWorld Wide Web

Abstract

fetched live from OpenAlex

Wireless Sensor Networks (WSNs) collect data through sensors and send it to the Cyberspace in the Cyber Physical system (CPS). With the growth of CPSs, researchers and system engineers need new means and measures to face the unexpected WSN security challenges. In this paper, we propose a 3-D taxonomy of WSN attacks, where each attack may be classified based on its Accessibility, Position and Type (APT). Accessibility represents the number of nodes under attack, whereas position and type represent the physical location of the node under attack and the type of attack, respectively. Our proposed classification helps network engineers better understand and identify the threats to their networks and systems. Through this taxonomy, 27 attack-scenarios are studied and quantified for further analysis. This paper proposes a novel quantification scheme, which can be used to understand the attacks' severity. We present five case studies to show how to use the proposed quantification scheme to evaluate the severity level of attacks across different application domains.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.196

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 science0.0000.000
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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