Enhanced Active Eavesdroppers Detection System for Multihop WSNs in Tactical IoT Applications
Bibliographic record
Abstract
In tactical Internet of Things (IoT) applications, the broadcast nature of wireless sensor networks (WSNs) makes it easy to eavesdrop on their traffic. Furthermore, adversaries can access the sensor nodes to intercept and eavesdrop on critical wireless transmission. Research in this area focuses on reducing the eavesdropping probability through specific methods, such as encryption and transmission power control. However, eavesdropper detection techniques in WSNs do not exist in the literature. This article proposes a novel enhanced active eavesdroppers detection (EAED) system for homogeneous multihop WSNs. The EAED system consists of a monitoring module and a detection engine module. The Monitoring module plays a vital role in the EAED system to provide accurate measurements for the detection engine module. We propose three monitoring architectures for this measurement: 1) static monitoring nodes; 2) unmanned aerial vehicles (UAVs)-based monitoring; and 3) neighborhood monitoring. To find the optimal locations for static monitoring nodes, we use a genetic algorithm (GA). We also use the Hamiltonian path planning to calculate the flight path for UAVs. The detection engine module utilizes a lightweight anomaly detection method that employs the$Z$-test method and runs on edge devices. We analyze and discuss the network overhead, advantages, and disadvantages of different monitoring architectures. According to the simulation results, the EAED system can detect active eavesdroppers with a high acrlong DR$({\ge }90\%)$and a low false-positive rate$({\leq }5\%$) and outstanding performance (${\mathrm{ AUC}}\approx 0.97$).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".