Resilient Synchronization for Insecure Markovian Jump Neural Networks to Mitigate Dual Cyber Attacks
Bibliographic record
Abstract
This study proposes a resilient asynchronous controller for Markovian jump neural networks, which can be impervious to the dual cyber attacks that act on actuators and sensors. The complicated occasions of uncertain system modes, actuator and sensor attacks, and unknown attack information are all considered. It is known that sensor attacks can generate corrupted signals to destroy the controller, and actuator attacks can maliciously tamper with the control signals. Mindful of such circumstances, a resilient controller is developed to defend against actuator and sensor attacks as well as to guarantee good synchronization performances. To overcome the unknowns of the occurred attacks, some new adaptive laws for adjusting attack parameters are introduced into the controller to assist with offsetting attack-induced influences. Under the designed controller, the synchronization error dynamic system is proven to be ultimately bounded within a known region, and then the obtained results are extended to address some other cases. Furthermore, a practical example of an analog resistance-capacitance network circuit and some comparative studies are demonstrated to verify the feasibility and superiority of the proposed controller.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".