Active Eavesdroppers Detection System in Multi-hop Wireless Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it