Monitoring and Detection of Malicious Adversarial Zero Dynamics Attacks in Cyber-Physical Systems
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
This paper is mainly concerned with monitoring and detection of zero dynamics (ZD) cyber attacks that are injected by malicious hackers and adversaries to safety critical cyber-physical systems (CPS). We consider a CPS system where the physical system (i.e., the plant) is represented by a linear time-invariant dynamical process. Specifically, we propose and provide a methodology for detecting zero dynamics cyber attacks through introducing an auxiliary system and detection filters. When compared to the currently available methods in the literature, the key advantage of our proposed strategy is that even if the attacker has complete knowledge of the CPS system including knowledge of our proposed approach, the introduced auxiliary system and filters, the attacker cannot design an undetectable attack that significantly and adversely impact stability and performance of the CPS system.
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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 | 0.000 | 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 it