Voltage hopping induced by bias injection attack against Kalman filter of BLDC motor
Bibliographic record
Abstract
A new type of bias injection attack against Cyber Physical System with closed loop controlled Brushless DC (BLDC) motor and Kalman filter in the feedback loop is simulated herewith involving high frequency and high amplitude for modulation of the bias. The attack results in significant voltage jumps with no visible affects on state variables, which we call “voltage hopping” effect. Implemented is operation of BLDC motor in position sensorless mode under Kalman filter drive. False data injection is conducted by substituting the angular velocity estimates from Kalman filter with distorted values. Conceptually, the attack is designed to mislead the controller and trigger the controller's effort to fix the distorted state of the system instead of the real one thus causing the controller to further distort the balance in the system. Herewith the introduction of high frequency component into the bias is tested in simulation experiment. It is shown that high frequency distortion strongly affects the operating voltage in the winding's circuits causing, as we named it, “voltage hopping” effect when significant changes of applied voltage occur at a frequency higher than cut off frequency of operating RL circuits. Moreover, it is demonstrated that balancing the parameters of the attack allows to reach equilibrium state in which voltage jumps are not reflected in the changes of BLDC motor operation, such as currents in the motor's windings as well as rotor's angular speed. This offers to the attacker the route to increase stealthiness of the attack, while still maintaining its harmful potential.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".