Are you for Real? Authentication in Dynamic IoT Systems
Bibliographic record
Abstract
Dynamic Internet-of-Things (IoT) systems are nonlinear cyber-physical systems that move around and operate in the physical environment under the control of stability laws in the cyber world. An example of such systems are Unmanned Aerial Vehicles (UAV s), or drones. In this environment, fake nodes can masquerade themselves as real nodes, to fool the command and control functions that can target resource management and lead to Denial-of-Service (DoS) attacks. In this paper, we present a novel authentication framework to identify fake nodes from the real ones by deriving and monitoring the stability function. More specifically, we exploit the Lyapunov stability function to validate the authenticity of a drone's physical behavior. We use training traces from real nodes to derive the stability function, then use it to authenticate traces at runtime. Our technique is implemented in a tool called Phoenix. We evaluate Phoenix with a system simulator as well as a real-world drone. We find that Phoenix takes about 50 ms to distinguish fake from real nodes, achieves a recall rate of over 96% and a precision rate of 95%, and can foil even determined attackers with limited computational resources.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".