Extended Pulsation Attack Against Kalman Filter Driven BLDC Motor Control System
Bibliographic record
Abstract
The extended version of the induced pulsation attackis introduced through modeling and simulation, targeting a closed-loop-controlled architecture of a Brushless DC (BLDC) motor driven by a Kalman filter. Previously, the induced pulsations in the attack were created by a constant gradient linear function of the distortion coefficient employed by the attacker. In the updated version, the attack persists even when interruptions are introduced to the linear growth of the distortion function, followed by resetting it. The objective of the presented study isto assess the potential for enhancing the attack’s effectiveness through this modification. The resulted findings confirm thattransforming the constant gradient linear function into a saw-tooth pattern leads to periodic repetitions of induced pulsations. Consequently, the attack can be extended indefinitely. Moreover,the attacker gains greater control over attack outcome by utilizing additional parameters introduced through the saw-like functionality, such as frequency and amplitude of saw-function. The study demonstrates two advantageous synchroniza-tion modes with the saw-function for the attacker: (1) modulatingcurrent, voltage, and angular speed in phase with the saw-function, providing a stronger attack but reduced covertness; (2) synchronizing current and voltage patterns with the first sub-harmonic of the saw-function while maintaining fundamental harmonic pace for angular speed modulation, resulting in improved covertness and consistent mechanical strains. Extended pulsation attack signatures collected in current, voltage, and angular speed domains aid in early detection through pattern recognition methods.
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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 | 0.001 |
| 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 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".