Tag Generation Using Chaotic Sequences for Physical-Layer Authentication
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
We consider in this work a physical layer authentication method in which a message authentication code, referred to as a tag, is transmitted along with the data message to provide a robust authentication method. This work diverges from previous work in the area when it comes to the tag generation method. While the previous works use methods based on cryptographic hash functions, our system employs unidimensional chaotic maps to generate these tags. Due to the loss of information about the initial condition of chaotic maps, we show that they are strong candidates for the tag generation process. We employ an information-theoretic approach to show that chaotic tags provide a positive lower bound on the unconditional security of the system even in a noiseless environment. To the best of our knowledge this is the first work where unconditional security is obtained independently of the noise power. Additionally, we calculate the probability of success for two active attacks to the authentication system: impersonation, substitution.
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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 | 0.000 | 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