An enhanced cross-layer authentication mechanism for wireless communications based on PER and RSSI
Bibliographic record
Abstract
Recently physical layer attributes and statistics have been exploited in securing wireless communications. However, one major obstacle of physical layer security techniques is that not all of these attributes are accessible in practical wireless communication platforms. More precisely, once the hardware of a physical transceiver is implemented, most of the physical layer attributes are not accessible due to the highly integrated circuits. Consequently, it becomes essential to develop implementable security enhancement techniques by utilizing all available attributes and statistics at different layers of wireless communication networks. In this paper, we consider the packet error rate (PER) and the received signal strength indicator (RSSI) in IEEE 802.11 networks to improve the wireless communication security. These two unique user and environment dependent attributes are readily available in most of the currently deployed IEEE 802.11 platforms. To enhance the spoofing attack detection capability, we propose a practical authentication scheme by monitoring and analyzing the PER and RSSI at the same time. The hypothesis testing model for the proposed authentication using PER and RSSI as two testing variables is presented. In addition, a decision rule for authentication, which is able to differentiate between a legitimate transmitter and a potential attacker by combining both attributes together, is developed. To evaluate the feasibility of our proposed scheme, lab experiments have been conducted using an IEEE 802.11g Atheros platform. The proposed authentication technique is validated by the experimental and simulation data. Our final authentication results confirm the improved spoofing detecting capability of the proposed technique over the single-variable based authentication.
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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 | 0.004 |
| 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".