Using support vector machines for detecting active spoofing attacks
Bibliographic record
Abstract
Physical layer security (PLS) has become an important topic in wireless communications for decades. Different from security solutions operating at the upper ISO layers, PLS solutions rely on the randomness of wireless channels to cope with eavesdroppers/spoofers [1-3]. In general, there are two common types of PLS concerns: (i) passive eavesdroppers conceal their presence while listening to the transmitted signals and (ii) active spoofers impersonate some legitimate users to break into the system. An active spoofer (i.e., an active E) is more malicious than a passive eavesdropper (i.e., a passive E) because the amount of information leaked to the active E is higher [3,4]. Thus, many works pay special attention to active spoofing, e.g., [3,5-7]. Since spoofers actively send their spoofing signals, there is a chance for us to detect their presence based on received signals.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".