Solution to the wireless evil-twin transmitter attack
Bibliographic record
Abstract
In a wireless network comprising some receivers and a truth-teller transmitter, an attacker adds a malicious evil-twin transmitter to the network such that the evil-twin lies about its true identity and transmits like the truth-teller transmitter in the network. The truth-teller transmitter may be a malicious transmitter as well, but it is honest in that it doesn't lie about its identity. The evil-twin uses the identity of the truth teller and transmits at the same time as the truth-teller. The receivers are bound to get confused about the location of the honest transmitter. We describe an algorithm to detect such a wireless evil-twin attack, and locate the truth-teller and the evil-twin transmitter. Four-square antennas are used by the receivers to detect an attack. RSS values measured at the receivers are used by Hyperbolic Position Bounding (HPB) to locate the transmitters in the wireless network with a degree of confidence. The performance of the algorithm is tested using a simulation of a wireless network.
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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".