Vulnerability Analysis of Torque Controlled PMSM Drives Against Sophisticated Data Integrity Attacks
Bibliographic record
Abstract
Electric vehicles (EVs) have become more intelligent and smarter with the incorporation of electronics and software which depends on sensor signals and networks. For better performance of the connected and autonomous EVs, several sub units have to be controlled in a centralized manner. However, this has made it more vulnerable to malicious cyber-attacks, since the sensors and networks are more prone to cyber-attacks. In this paper, the impact of various sensor data integrity attacks on torque controlled permanent magnet synchronous motor (PMSM) based electric drive system (EDS) has been studied through MATLAB simulations. Different sensor data integrity attacks are modelled and analysed with the help of predefined performance metrics for PMSM-based EDS under different torque control strategies. The simulations and analysis can help in understanding the impacts of sensor data integrity attacks on EDS and hence can pave way for its effective mitigation through predictive control approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".