Security Index of Linear Cyber-Physical Systems: A Geometric Perspective
Bibliographic record
Abstract
This paper is mainly concerned with developing security indices for linear cyber-physical systems (CPS). The approaches for computing security (and consequently vulnerability analysis) of CPS in the literature are based on algebraic methods and system matrices. In this paper, for the first time in the literature we formally address the security index analysis and computation from a geometric system theory perspective. This point of view enables one to develop an algorithm for computing an upper bound on the security index having a linear time complexity with respect to dimension of the system (i.e., O(n)). This is a significant improvement compared to the currently available approaches in the literature that have polynomial time complexity. Unlike the approaches in the literature our methodology does not need any restriction on the representation of the system. Moreover, the geometric approach provides a tool to formally analyze the attack signals injected to the CPS by introducing a new type of attack that is more sophisticated than zero dynamic attacks. Finally, we illustrate our proposed methodology through a numerical 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".