Vehicle Dynamics Modelling and Simulation for Use in the Development of a Self-Healing Auto Cyber Security System (SHACS) Proof-of-Concept
Bibliographic record
Abstract
All modern vehicles utilise Electric Control Units (ECUs) to control the electronics of the vehicle.The advancement of autonomous vehicles and systems has significantly increased the number of ECUs present, thereby increasing need for cybersecurity.This thesis introduces the foundation for the integration of a Self-Healing Auto Cyber Security System (SHACS), designed to detect and correct attacks on the vehicle's Controller Area Network (CAN).The foundation builds upon a 10-degree-of-freedom (DOF) vehicle dynamics model with driver, sensor, and novel steer-by-wire models to represent the vehicle's physics.The new steering mechanism was designed by using servo motors to steer each wheel independently, increasing the signals on the CAN.The process of selecting a design for the steering mechanism was highlighted.The model was implemented in MATLAB/Simulink, then was integrated into a set of BeagleBone Blacks, a single board computer, for CAN and SHACS integration.A sample SHACS uses a unique detection and correction methodology, which utilises a voting mechanism to ensure the signal validity and the vehicle's continued safe operation under attack.The algorithm takes three time-steps to correct the system from an attack.System validation was achieved by using standardised vehicle paths and by comparing the simulated responses to the un-attacked Simulink model's results, demonstrating the sample SHACS's ability to protect against attacks.This thesis proposes a framework for simultaneously developing vehicle dynamics and cybersecurity systems that could be integral to the development of secure automotive technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.010 | 0.003 |
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".