HTM: Hierarchical Trust Management for Software-Defined WSNs
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
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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