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Record W4391690127 · doi:10.3389/fcomp.2023.1125124

Secure dynamic event-triggering control for consensus under asynchronous denial of service

2024· article· en· W4391690127 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

VenueFrontiers in Computer Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsYork UniversityDefence Research and Development CanadaConcordia University
FundersMinistère de la Défense Nationale
KeywordsDenial-of-service attackAsynchronous communicationEvent (particle physics)Computer securityComputer scienceDenialService (business)Computer networkDistributed computingBusinessPsychologyOperating systemThe Internet

Abstract

fetched live from OpenAlex

Introduction This article proposes a secure implementation for consensus using a dynamic event-triggered control (DETC) scheme for general autonomous multi-agent systems (MAS) under asynchronous (distributed) denial of service (DoS) attacks. The asynchronous DoS attacks can block each communication channel independently in an unknown pattern. Depending on the impact of DoS on the communication topology, the attacks are categorized into (i): connectivity-preserved DoS (CP-DoS), and (ii): connectivity-broken DoS (CB-DoS). In CP-DoS, the operating communication topology remains connected. On the other hand, in CB-DoS the adversary breaks the communication graph into isolated sub-graphs. Methods The DETC scheme is employed to reduce the control updates for each agent. To guarantee consensus under both the CP-DoS and CB-DoS, a linear matrix inequality (LMI) based optimization approach is proposed, which simultaneously designs all the unknown DETC parameters as well as the state feedback control gain. Results The proposed optimization method prioritizes the minimum inter-event interval (MIET) between consecutive control updates. The trade-off between relevant features of the MAS, namely the consensus convergence rate, intensity of control updates, and level of resilience to DoS can be handled by the proposed optimization. Discussion Simulation results quantify the effectiveness of the proposed approach, showcasing its ability to maintain secure consensus in MAS under varying DoS attack scenarios.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.249
Teacher spread0.241 · 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