Deep Deterministic Policy Gradient-Based Physical Layer Authentication Scheme Under Unknown Attacking Environment
Bibliographic record
Abstract
Physical layer authentication (PLA) is considered as a promising method to resist spoofing attacks, where the stochastic features of wireless channels are used to detect attackers. Most of the existing PLA schemes assume that the prior information of attackers is known by the receiver, which might not be realized in realistic networks. To address this issue, we propose a deep deterministic policy gradient based PLA (DPLA) scheme to identify legitimate transmitters and attackers under unknown attacking environment. Specifically, the deep deterministic policy gradient approach is employed in the proposed DPLA scheme, where two types of deep neural networks are used to adaptively adjust the authentication strategy in continuous action space by learning both the policy and the state-action value. Moreover, the double-Q approach and the delayed update policy network are integrated into the proposed scheme to reduce the overestimation bias in the value estimation process and ensure the stability of the policy learning. Simulation results show that the proposed scheme can achieve a substantial performance gain over several reference schemes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".