Drones' Face off: Authentication by Machine Learning in Autonomous IoT Systems
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".