Linear-Dynamic Predictor Based Defense Against Current Drainage Cyber Attack of BLDC Motor Control System
Bibliographic record
Abstract
Using modeling and simulation tools, we have demonstrated that the basic predictor module, which utilizes linear-dynamic predicting algorithms, has the potential to offer defense against bias injection type of cyber-attacks targeted at closed-loop-controlled systems, such as Kalman filter-driven brushless DC (BLDC) motors. Specifically verified is scenario of introduced earlier current drainage attack with full transparency for the defender, whose activity is shown to be able to counteract the manipulation game from the attacker. The attacker manipulation with distortion coefficient modifying angular speed estimates is shown to trigger significant growth of operating motor currents with accumulating effect on increasing speed of the rotor. Moreover, an abrupt termination of the attack is shown to result in even stronger currents increase in the forced braking process, creating potentially more damaging circuits overloads (to be called "attack withdrawal syndrome"). The defender is shown to be able to defeat the attacker within an asymmetric Stackelberg game relying on linear-dynamic predictor to preemptively compensate the distortion of the angular speed estimates. Demonstrated is potential for the defender to effectively negate the current drainage attack itself as well as its immediate consequence, such as attack withdrawal syndrome. Out of the reach for the defender remains the attack initiation impulse, whose damaging impact needs to be limited via dedicated algorithm with separate anomaly detector.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".