Lightweight Continuous Authentication via Intelligently Arranged Pseudo-Random Access in 5G-and-Beyond
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
Conventional authentication techniques based on cryptography and computational hardness are facing growing challenges for deployment in resource-constrained Internet-of-Things (IoT) devices. The dramatically increased security overhead and latency from the inherent computational processing make these conventional static security techniques undesirable for emerging machine communications. In this paper, we propose a novel lightweight continuous authentication scheme for identifying multiple resource-constrained IoT devices via their pre-arranged pseudo-random access time sequences. A transmitter will be authenticated as legitimate if and only if its access time sequential order is matched with a pre-agreed unique pseudo-random binary sequence (PRBS) between itself and the base station. The seed for generating the PRBS between each transceiver pair is acquired by exploiting the channel reciprocity, which is time-varying and difficult for a third party to predict. Hence, the proposed scheme provides seamless protection for legitimate communications by refreshing the seeds adaptively without incurring long latency, complex computation, and high communication overhead. Our results show that the proposed scheme achieves high entropy and low bit mismatch rate. Finally, we demonstrate the superiority of our scheme over the existing schemes in quantization performance, authentication performance, and computation cost.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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