HTM: Hierarchical Trust Management for Software-Defined WSNs
Bibliographic record
Abstract
Software-Defined WSNs (SDWSNs) have attracted considerable attention as they can provide more flexible network management compared with traditional WSNs. However, they also bring a new security issue, i.e., a sensor node can be easily compromised and behave maliciously to perform arbitrary action, e.g., dropping received messages, that degrades the availability of SDWSNs without being detected. To address the security issue, we propose a Hierarchical Trust Management scheme, named HTM. In HTM, a reputation-based mechanism is designed and utilized for detecting sensor nodes' malicious behavior, such as black-hole attack (dropping all received packets), selective forwarding attack (dropping partially received packets and forwarding the rest), and Denial-of-Service (DoS) attack (sending abundant but useless packets continuously). At each level of the hierarchical system, the trustworthy of each node is evaluated and the malicious behavior is detected. Through extensive simulation, we demonstrate that the HTM scheme is capable to detect malicious nodes that perform the aforementioned 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".