Sampled-Data Consensus for Multiagent Systems Over Semi-Markov Switching Networks Under Denial-of-Service Attacks
Bibliographic record
Abstract
This article investigates the almost sure consensus (ASC) problem for sampled-data multiagent systems (MASs) operating over semi-Markov switching networks (SMSNs) and facing different types of denial-of-service (DoS) attacks. During real-time information exchange among agents, communication failures between agents occur randomly, which may result in each possible network topology occurring with a certain probability, and its sojourn time is also stochastic. This necessitates the consideration of a more general switching signal to describe the stochastic switching phenomenon of networks. In pursuit of this goal, a semi-Markov chain is introduced to characterize the switching signal of stochastic interaction networks, whose sojourn time distribution allows for arbitrary continuous-time distribution and depends on the current and next state. Additionally, this article delves into the impact of two distinct types of DoS attacks on MASs. The first type involves random DoS attacks, which are also modeled by a semi-Markov chain to capture the stochastic nature of attack durations. The second type is deterministic DoS attacks, characterized by their frequency and duration. The proposed new stochastic analysis method, based on the law of large numbers, is used to analyze the ASC for MASs featuring SMSNs under the DoS attacks. The effectiveness of the proposed approach is demonstrated by evaluating the results obtained from two illustrative numerical examples.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".