An Efficient UAV Hijacking Detection Method Using Onboard Inertial Measurement Unit
Why this work is in the frame
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Bibliographic record
Abstract
With the fast growth of civil drones, their security problems meet significant challenges. A commercial drone may be hijacked by a GPS-spoofing attack for illegal activities, such as terrorist attacks. The target of this article is to develop a technique that only uses onboard gyroscopes to determine whether a drone has been hijacked. Ideally, GPS data and the angular velocities measured by gyroscopes can be used to estimate the acceleration of a drone, which can be further compared with the measurement of the accelerometer to detect whether a drone has been hijacked. However, the detection results may not always be accurate due to some calculation and measurement errors, especially when no hijacking occurs in curve trajectory situations. To overcome this, in this article, we propose a novel and simple method to detect hijacking only based on gyroscopes’ measurements and GPS data, without using any accelerometer in the detection procedure. The computational complexity of our method is very low, which is suitable to be implemented in the drones with micro-controllers. On the other hand, the proposed method does not rely on any accelerometer to detect attacks, which means it receives less information in the detection procedure and may reduce the results accuracy in some special situations. While the previous method can compensate for this flaw, the high detection results also can be guaranteed by using the above two methods. Experiments with a quad-rotor drone are conducted to show the effectiveness of the proposed method and the combination method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it