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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".