Passive and Active Wireless Device Secure Identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Secure wireless device identification is necessary if we want to ensure that any transmitted data reach only a desired receiver. However the fact that wireless communications are by nature broadcast creates unique challenges such as identity theft, eavesdropping for data interception, jamming attacks to disrupt legitimate transmissions, etc. This paper proposes a new integrated radioprint framework (IRID) that has two main components. First, we propose a machine learning-based radio identification solution that relies on hardware variabilities of internal components of the transmitter caused during manufacturing, allowing us to achieve passive device identification. Second, we introduce a new kind of covert channel, based on variations in the emitted signal strength, which allows us to implement unique active device identification. We evaluate our proposed framework on an experimental test-bed of 20 identical WiFi devices. Although our experiments deal only with IEEE 802.11b, the approach can easily be extended to any wireless protocol. The experimental results show that our proposed solution can differentiate between network devices with accuracy in excess of 99% on the basis of a standard-compliant implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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