Active Eavesdroppers Detection System in Multi-hop Wireless Sensor Networks
Bibliographic record
Abstract
Eavesdropping attacks can threaten the privacy, confidentiality, and authenticity of Wireless Sensor Networks (WSNs). Since the broadcast nature of the wireless channel is vulnerable to overhearing by adversaries, detection of the presence of eavesdroppers in wireless networks can mitigate the impacts of more harmful attacks. Traditionally, researchers have tried to decrease the risk of covert eavesdropping by cryptographic protocols, information-theoretic solutions, or controlling transmission range. These approaches are not suitable for the resource-limited WSNs. In this paper, we propose a novel Active Eavesdroppers Detection (AED) system for multi-hop WSNs. Our proposed system utilizes an out-of-band Unmanned Aerial Vehicle (UAV)-assisted monitoring system in WSNs to measure intranode delays. In addition, the detection system is equipped with a lightweight detection engine, which runs at edge devices, using the Z-test algorithm. We show the effectiveness of our proposed system through simulations. The results show a high detection rate and a low false-positive rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".