Correlation-Based Deception Attack Detection for Cyber–Physical Control Systems With Multiple-Security Level Transmission Channels
Bibliographic record
Abstract
In this article, the deception attack detection problem is studied in scenarios involving multisecurity level transmission channels. Powerful attackers can construct stealthy deception attacks by exploiting data from reliable and unreliable channels. From the perspective of data correlation, we develop three detection schemes with different resource consumption. First, a fully security channel is utilized to establish innovation-based time-varying data correlation, which triggers residual covariance variation under attacks. Second, a noise-encryption mechanism is introduced without requiring the fully security channel. For the initial two methods, we propose a targeted optimization method to improve the detection performance by exploiting the quantified residual covariance variation. Third, we propose a time-shift coding method from the perspective of dynamic system stability, which is rigorously proved to be sensitive to attack behavior. For these proposed methods, we quantify the residual covariance variation induced by attacks and achieve detection by the$\chi ^{2}$test and generalized likelihood ratio test. Finally, the efficiency and reliability of these detection schemes are validated by examples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".