New LLRT-Based Methods for Active Eavesdropper Detection in Cell-Free Massive MIMO
Bibliographic record
Abstract
In this paper, the problem of active eavesdropper detection is considered for a cell-free massive multiple-input multiple-output (m-MIMO) system which is attacked by an active eavesdropper within the uplink training phase, also called pilot spoofing attack. Two methods based on log-likelihoodratio tests (LLRT), one in a centralized and the other in a decentralized fashion, are proposed to detect the signal abnormality. The methods take advantage of a special protocol in which the legitimate users switch to an off-mode irregularly, without significantly affecting the spectral efficiency of the data transmission. The protocol is directly applicable to environments with low to moderate mobility, and can be extended to high mobility through a simple rearrangement of available pilot sequences among users if needed. More importantly, the proposed methods impose low fronthaul overhead which is imperative for a cellfree m-MIMO system with a large number of access points (APs). A closed-form expression for the joint probability density function (PDF) of the processed received signals conditioned on the alternative hypothesis, which is essential for the implementation of LLRT-based detection methods, is also derived. Through an asymptotic analysis, it is shown for the proposed methods that the detection and false-alarm probabilities approach to one and zero, respectively as the number of APs goes to infinity. Numerical results reveal that both methods significantly outperform a recent approach in terms of false-alarm rate with negligible degradation in the per user uplink spectral efficiency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".