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Record W4392309528 · doi:10.1109/tii.2024.3363091

Resilient Control of CPSs Under Mixed DoS Attacks: A Type-Dependent ADT Approach

2024· article· en· W4392309528 on OpenAlexafffund
Hui-Ting Wang, Kunwu Zhang, Yong He, Yang Shi

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2024
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsComputer science

Abstract

fetched live from OpenAlex

This article studies the resilient control against mixed denial of service (DoS) attacks for cyber-physical systems (CPSs). Different from existing results, this work considers the presence of both zero- and hold-input attacks, where a unified model is introduced to describe mixed DoS attacks. Upon this model, the original CPS is reformulated as a switched system subject to a time-varying delay. To characterize the occurrence frequency and duration of zero- and hold-input attacks, the type-dependent average dwell time (ADT) switching is adopted. In the meantime, multiple discontinuous Lyapunov functions (MDLFs) suitable to the type-dependent ADT switching are employed. By virtue of the switching scheme and MDLFs, a piecewise feedback controller is designed to guarantee global uniform exponential stability and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> performance of the closed-loop system. In addition, the developed control law is extended to an observer-based version, accommodating the case when the system state is not fully measurable. Finally, the effectiveness of our theoretical results is verified by two numerical examples.

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: none
Teacher disagreement score0.924
Threshold uncertainty score0.715

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 integrity0.0000.001
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 head0.031
GPT teacher head0.240
Teacher spread0.209 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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