Addressing security in drone systems through authorization and fake object detection
Bibliographic record
Abstract
There now exists more than eight billion IoT devices with expected growth to reach over 22 billion by 2025. IoT devices are comprised of sensor and actuator components which generate live-stream data and share information via a common communication link, e.g., the Internet. For example, in a smart home, a number of IoT devices such as a Google Home/Amazon Alexa, smart plugs, security cameras, a garage door, and a thermostat connect to the WiFi network to routinely communicate with each other, share information, and take actions accordingly. However, a main security challenge is protecting shared information between authorized devices/users while distinguishing real objects from fake ones in the network. Such a challenge aggravates man-in-the-middle, and denial-of-service vulnerabilities. To defend such concerns, in this thesis, we first propose an authorization framework called Dynamic Policy-based Access Control (DynPolAC) as a model for protecting information in dynamic and resource-constrained IoT systems. We focus our experiments with DynPolAC on an IoT environment comprised of drones. DynPolAC achieves more than 7x speed performance improvements in authorization when compared to previously proposed methods for resource-constrained IoT platforms such as drones. Secondly, in this thesis, we implement a method called Phoenix to detect fake drones in an IoT network from real drones. We experimentally train and derive Phoenix from a control function called the Lyapunov stability function. We evaluate Phoenix for drones using an autopilot simulator as well as flying a real drone. We find that Phoenix takes about 50 ms to distinguish real drones from fake ones, while by asymmetry, it could take days for motivated attackers to reconstruct Phoenix. Phoenix also achieves a precision rate of 99.55% to detect real drones and a recall rate of 99.84% to detect fake drones.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".