Attack and Defense on Aircraft Trajectory Prediction Algorithms
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-4027.vid The aviation industry needs lead to an increase in the number of aircraft and their flights. When the number of flights within an airspace increases, the chance of a mid-air collision (or collision) increases. Collision Avoidance Systems such as the Traffic Alert and Collision Avoidance System (TCAS) and Airborne Collision Avoidance System (ACAS) are currently used to alert pilots for potential mid-air collisions. The TCAS and the ACAS use algorithms to perform Aircraft Trajectory Predictions (ATPs) to detect potential conflicts between aircrafts. In this paper, three different aircraft trajectory prediction algorithms are discussed by using existing aircraft trajectory data containing multiple different aircraft types with different flight patterns. With this dataset, the future aircraft heading is predicted using the latitude, longitude, altitude, velocity and time. The three algorithms’ performances were evaluated in terms of their accuracy and robustness. These trajectory prediction algorithms were as well evaluated in the case of adversarial samples. Although algorithms can find reliable ATPs, earlier research has shown that they are also vulnerable against adversarial attacks that produce adversarial samples. Adversarial samples are similar to original samples from the dataset. These perturbations can cause trained algorithms to predict unreliable trajectories, which cause a security threat for learning-based trajectory algorithms, as adversarial attacks can result in intentional collisions. Adversarial training is applied as defense method in order to increase the robustness ATPs algorithms against the adversarial samples. The adversarial samples are included in the training data in order to make the algorithm more robust in the case of an adversarial attack. The findings in this research show that, comparing the three algorithm’s performance, the extreme gradient boosting algorithm is most robust against adversarial samples and adversarial training will benefit the robustness of the algorithms against lower intense adversarial samples. The contributions of this paper concern the evaluation of different aircraft trajectory prediction algorithms, the exploration of the effects of adversarial attacks, and mainly the effect of the defense against adversarial samples with low perturbation compared to no defense mechanism.
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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 | 0.000 | 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