A Diagnosis Method of Inverter Anomalies under DoS Attack Based on Interval Sliding Mode Observer
Bibliographic record
Abstract
To improve the robustness and accuracy of cyber-physical system fault diagnosis, a diagnosis method for DoS (denial of service) attacks of inverters based on interval sliding mode observer is proposed in this paper. The mixed logical dynamic (MLD) model of the inverter is established by using the current flow direction of the switch under normal working and DoS attacks conditions. A current interval sliding mode observer is designed, which is constructed via a convex weighted sum of the current estimators of the upper bound sliding mode observer and the lower bound sliding mode observer. The designed observer is then used to estimate the normal three-phase current value of the inverter. The current residual which is obtained by comparing the current from the actual system and the observer is used to detect DoS attacks. The residual information table is established according to the attacked information contained in the residual to perform attacked location. The designed current interval sliding mode observer can not only improve the convergence speed of the interval observer, but also reduce the chattering effectively and improve the robustness of the cyber-attacks diagnosis system.
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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 | 0.000 |
| 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) | 0.001 | 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 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".