AICN: An Efficient Algorithm to Identify Compromised Nodes in Wireless Sensor Network
Why this work is in the frame
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Bibliographic record
Abstract
Wireless sensor networking is an emerging technology, which potentially supports many emerging applications for both civilian and military purposes, ranging from environmental monitoring to battlefield surveillance. However, since sensor nodes are inexpensive devices, which could be easily compromised and controlled by an adversary, the compromised nodes could report false sensed results and degrade the reliability of the whole network. Therefore, how to identify these compromised nodes in a wireless sensor network is a very important security issue. To solve this problem, we propose an efficient algorithm, called AICN, to logically identify the compromised nodes in an efficient and effective way. Based on the network reliability estimation (NRE), we also present its enhanced version to further improve the efficiency.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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