Designing Optimal Stealthy False Data Injection Attacks in Cyber-Physical Systems: Leveraging Historical Data and Kullback–Leibler Divergence Constraints
Bibliographic record
Abstract
In the rapidly evolving landscape of Cyber-Physical Systems (CPS), understanding potential vulnerabilities through the design of sophisticated attack strategies is crucial for developing robust defense mechanisms. This paper focuses on formulating innovative False Data Injection (FDI) attack strategies that leverage current and historical data under relaxed stealthiness constraints, measured by the Kullback-Leibler Divergence (KLD). By exploring the trade-offs between attack performance and detection risk, we propose two types of attack policies that not only enhance the effectiveness of the attacks but also offer practical implementation benefits. The optimal attack parameters are derived analytically, enabling efficient offline pre-calculation and real-time deployment. Finally, simulation studies on a satellite system validate the superiority of our strategies over existing methods, demonstrating the ability to maximize disruption while maintaining stealthiness. This research not only deepens our understanding of CPS vulnerabilities but also lays the groundwork for more resilient defense strategies by anticipating and countering sophisticated attacks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".