Kalman Filter Based Secure State Estimation and Individual Attacked Sensor Detection in Cyber-Physical Systems
Bibliographic record
Abstract
In this paper we propose two real-time attack detection and secure state estimation algorithms, namely Rolling Window Detector (RWD) and Novel Residual Detector (NRD). These algorithms are basically developed based on Kalman state estimation. In the former, we present a statistical testing approach which is handled over a finite time horizon T to detect individual attacked sensors. The latter extends the X2-detector to be able to detect individual compromised sensors. Both methods then will be employed together with a modified version of Kalman filter to perform a secure state estimation with a relatively low estimation error. Efficiency of the algorithms will be assessed in both unstealthy and stealthy scenarios. Productivity of the methods will be underlined in the stealthy case, which is of much more significance among cyber-security challenges. Simulation results on an IEEE 14-bus power grid test system along with a comprehensive comparison between the performance of RWD and NRD with a recently introduced tool, which is the only other method that tries to detect individual attacked sensors, proves the effectiveness of the algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".