Secure dynamic event-triggering control for consensus under asynchronous denial of service
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it