Finite-Time Dissipative Tracking Control of Semi-Markov Jump Systems Under Multi-Channel Hybrid Attacks
Bibliographic record
Abstract
This study examines the output tracking control of discrete-time networked semi-Markov jump systems (SMJSs) under cyber-attacks in the framework of finite-time control methodology. Different from most networked systems that employ single-channel communication, this work considers the case of multi-channel communication in the controller-to-actuator networks. Aimed at better reflecting the practical situation, a type of hybrid attacks is taken into consideration, which is a mixture of denial-of-service attacks and false data injection attacks. Subsequently, the dynamic characteristics of hybrid attacks among multiple channels are modeled by two stochastic processes. The goal is to design a state feedback controller such that the resulting closed-loop system is not only finite-time boundedness with dissipative performance but also has robustness against hybrid attacks. Finally, the effectiveness of the proposed novel controller design method is verified by an illustrative example.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".