Robust Covert Attack Strategies and Their Detection for Switched Cyber-Physical Systems
Bibliographic record
Abstract
In this paper, first, a robust covert attack is designed for switched cyber-physical systems with synchronous switching from the attacker-viewpoint in which the attacker makes the system follow their specified reference signal while it remains stealthy in the monitoring system. This attack is defined in the form of $H_{\infty}$ control problem such that the objectives of the attacker will be achieved. Next, as a defender, a novel detection method will be presented that can detect covert attacks in the switched system. In the proposed method, we do not need any secure channel and even in the case that the attacker can find the model of the auxiliary system and injects another signal on the corresponding communicated information, the cyber-attack can be detected. The only protected information in the proposed method is the considered delay in the mode information of the auxiliary system that needs to be exactly estimated by the attacker to have a completely stealthy attack. Simulation results demonstrate and illustrate the significant performance and capabilities of the proposed method.
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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 | 0.000 |
| 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".