Drones' Face off: Authentication by Machine Learning in Autonomous IoT Systems
Why this work is in the frame
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Bibliographic record
Abstract
Autonomous Internet-of-Things (IoT) are comprised of moving objects such as drones and rovers that use self-control techniques to accomplish a mission while following a path. However, losing control in such systems usually by spoofing their sensors or hijacking with misleading commands can lead to catastrophic safety consequences. In this paper, we close the gap by authenticating the behavior of autonomous IoT systems during operation. In particular, we check the behavior of a moving IoT object, e.g., a drone, by evaluating its time-series telemetry traces during the flight. We examine three different machine-learning algorithms for this purpose, namely, K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). Our results show that KNN is the best method of the three selected techniques for authentication in dynamic IoT systems, e.g., drones. We achieved 93.4% in precision rate and 100% recall rate with KNN.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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