An Effective Hybrid Approach for Detection of False Data Injection Attacks in Connected Battery Systems with Noisy Measurements
Bibliographic record
Abstract
In this paper, an effective method based on adaptive extended Kalman filter (AEKF) is proposed for detection of random FDIs against battery state-of-charge algorithms on cloud battery management platforms. First, the battery model is established and used with the AEKF to predict the battery response. Second, a residual signal (RS) is defined as the difference between the AEKF-based estimated battery voltage and the received voltage measurement. The FDIs are then detected based on a hybrid detection criterion mixing the Chi-squared test and Euclidean detector. The proposed mixed strategy improves the detection accuracy in terms of false negatives and false positives caused by noises and changes in battery operation. Regarding the latter point, the AEKF is equipped with a dedicated recursive least squares filter to accommodate real-time model changes. The proposed algorithm is developed and verified based on actual battery data related to high-capacity lithium-ion cells. The method is exposed to different case studies considering normal and attack conditions and a remarkable detection accuracy of about 98% is attained with no false positive in the presence of current and voltage noises up to ±10 mA and ± 3 mV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".