Forensic outlier detection for Cognitive Radio Networks
Bibliographic record
Abstract
We consider forensic outlier detection instead of traditional outlier detection to enforce spectrum security in a Cognitive Radio Network (CRN). We investigate a CRN where a group of sensors report their local binary decisions to a Fusion Center (FC), which makes a global decision on the availability of the spectrum. To ensure the truthfulness of the sensors, we examine the reported decisions in order to determine whether a specific sensor is an outlier. We propose several optimal detectors (for known parameters) and suboptimal detectors (for the practical cases where the parameters are unknown) to detect three types of outlier sensors: 1) selfish sensor, which reports the spectrum to be occupied when locally detects its vacancy, 2) malicious sensor, which reports the spectrum to be vacant when locally detects its occupancy, 3) malfunctioning sensor, whose reports are not accurate enough (i.e., its performance is close to random guessing). We evaluate the proposed detectors by simulations. Our simulation results reveal that the proposed detectors significantly outperform the Grubb's test. Since the unknown or untrustworthy parameters are accurately estimated by the FC, the proposed suboptimal detectors do not require the knowledge of the spectrum statistics and are insensitive to the parameters reported by the suspected user. These detectors can be used by government agencies for forensic testing in policy control and abuser identification in CRNs.
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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".