A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks
Bibliographic record
Abstract
This article proposes a protocol layer trust-based intrusion detection scheme for wireless sensor networks. Unlike existing work, the trust value of a sensor node is evaluated according to the deviations of key parameters at each protocol layer considering the attacks initiated at different protocol layers will inevitably have impacts on the parameters of the corresponding protocol layers. For simplicity, the paper mainly considers three aspects of trustworthiness, namely physical layer trust, media access control layer trust and network layer trust. The per-layer trust metrics are then combined to determine the overall trust metric of a sensor node. The performance of the proposed intrusion detection mechanism is then analyzed using the t-distribution to derive analytical results of false positive and false negative probabilities. Numerical analytical results, validated by simulation results, are presented in different attack scenarios. It is shown that the proposed protocol layer trust-based intrusion detection scheme outperforms a state-of-the-art scheme in terms of detection probability and false probability, demonstrating its usefulness for detecting cross-layer attacks.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".