Evaluating Channel Based Relay Attack Detection in the Near-Field
Bibliographic record
Abstract
Relay attacks are able to circumvent challenge-response authentication protocols, which are commonly used with RFID and NFC devices to securely interact with the physical world. Ultra-wideband round-trip time distance-bounding is the most prominent solution, but it provides low data rates, short communication distances, and requires dedicated hardware. In this paper we propose a novel symmetric-key protocol that simultaneously provides challenge-response authentication and relay attack detection in the near-field and far-field, through the utilization of a random FIR filter as a challenge. The protocol utilizes the channel estimation in an OFDM communication system to perform the evaluations. To evaluate the performance of the protocol hardware experimentation on software-defined radios is done to observe the false-positivity and false-negativity rates of relay detection under normal communications and a relay attack. The results show that our proposed protocol can detect relay attack in the near-field (≤ 10% false-negativity) at various signal-to-noise ratios.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".