Eavesdropping attack in collaborative wireless networks: Security protocols and intercept behavior
Bibliographic record
Abstract
In this paper, we investigate security issues in a collaborative wireless network in the presence of eavesdropping attacks, where multiple amplify-and-forward (AF) relays are exploited to secure the message transmission between legitimate users. We first consider the multiple AF relays all participating in assisting the transmission from source to destination, which is called all-relay based collaborative transmission scheme as denoted by all-relay scheme for notational convenience. We also propose the best-relay transmission scheme in which only the single “best” relay is selected to help the source transmit messages to destination. We then analyze the intercept behavior in wireless networks and evaluate intercept probabilities of the proposed all-relay and best-relay schemes as well as the conventional direct transmission without relay in a Rayleigh fading environment. Numerical results show that the best-relay transmission scheme always outperforms the all-relay and direct transmission schemes in terms of intercept probability. It is also shown that as the number of eavesdroppers increases, the intercept probabilities of both all-relay and best-relay schemes increase. Moreover, the intercept probability performance of all-relay and best-relay schemes significantly improves with an increasing number of relays, implying the advantage of exploiting multiple relays against eavesdropping attacks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".