Detection and Identification of Cyber-Attacks in Switched Cyber-Physical Systems
Bibliographic record
Abstract
This paper deals with the detection and identification of the type of cyber-attack in switched Cyber-Physical Systems (CPS) operating under the synchronous switching conditions. The suggested approach involves an auxiliary system with a particular structure on the plant side, along with three switched observers on the Command and Control (C& C) side. To accomplish detection and identification objectives, the output of the auxiliary system needs to be communicated as well as real measurements of the system. Using the communicated information, three sets of residuals are obtained based on output estimation errors of two Switched Unknown Input Observers (SUIO) and a Switched Luenberger Observer (SLO). In the suggested approach, no secure channel or system is required and the least possible amount of information needs to be secured, which corresponds to the delay between the mode of the plant and auxiliary systems. Various attack scenarios such as covert attack, False Data Injection (FDI) attack on the input channels, FDI attack on the measurement channels and zero-dynamics attacks can be efficiently detected and identified by the proposed methodology. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".