Randomly directed exploration: An efficient node clone detection protocol in wireless sensor networks
Bibliographic record
Abstract
Node clone attack, that is, the attempt by an adversary to add one or more nodes to the network by cloning captured nodes, imposes a severe threat to wireless sensor networks. Several distributed detection protocols have been proposed against this attack. However, all of them rely on too strong assumptions and cannot be efficiently applied to most of sensor networks. In this paper, we propose an innovative randomly directed exploration protocol to detect the node clone. Each node need only know its neighbors' information, and then collaborates to forward claiming messages, trying to find out clone. No any specific routing protocols or infrastructures are demanded in the proposed protocol. Therefore, it is highly practical in the general sensor network applications. In addition, the memory requirement of the protocol is almost optimal. Furthermore, the protocol consumes relatively low communication overload, which is not inferior to any previous schemes. The simulation results show that the protocol can achieve high detection probability. Overall, the proposed protocol outweighs previous approaches in terms of practicability and performance.
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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) | 0.001 | 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 | 0.002 |
| 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".