Bibliographic record
Abstract
Robotic vehicles (RVs) are cyber-physical systems that operate in the physical world under the control of software functions. They are increasing in adoption in many industrial sectors. RVs rely on sensors and actuators for system operations and navigation. Control algorithm based estimation techniques have been used in RVs to minimize the effects of noisy sensors, prevent faulty actuator output, and recently, in detecting attacks against RVs. In this paper, we propose three kinds of attacks to evade the control-based detection techniques and cause RVs to malfunction. We also propose automated algorithms for performing the attacks without requiring the attacker to expend significant effort or know specific details of the RV, making the attacks applicable to a wide range of RVs. We demonstrate these attacks on ArduPilot simulators and two real RVs (a drone and a rover) in the presence of an Intrusion Detection System (IDS) using control estimation models to monitor the runtime behavior of the system. We find that the control models are incapable of detecting our stealthy attacks, and that the attacks can have significant adverse impact on the RV's mission (e.g., cause the RV to crash or deviate from its target significantly).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.038 |
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".