CAT: Building Couples to Early Detect Node Compromise Attack in Wireless Sensor Networks
Bibliographic record
Abstract
Node compromise attack is a serious threat to the successful deployment of wireless sensor networks. It is a multiple-stage attack, which usually consists of three stages: physically capturing and compromising sensor nodes; redeploying the compromised nodes back to the sensor network; and compromised sensor nodes rejoining the network and launching attack. Over the last few years, much previous work has tackled the node compromise attack in the late stage, either in the second stage or in the third stage. As a result, the protection measures are often ineffective. In this paper, we will make the first effort on addressing the node compromise problem in the first stage, and present a new couple-based scheme to detect the node compromise attack in early stage. Specifically, after sensor nodes are deployed, they first build couples in ad hoc pattern. Then, the nodes within the same couple can monitor each other to detect any node compromise attempt. Extensive simulation results are given to demonstrate the high detection rate of the proposed scheme.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".