Localized Multicast: Efficient and Distributed Replica Detection in Large-Scale Sensor Networks
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
Due to the poor physical protection of sensor nodes, it is generally assumed that an adversary can capture and compromise a small number of sensors in the network. In a node replication attack, an adversary can take advantage of the credentials of a compromised node to surreptitiously introduce replicas of that node into the network. Without an effective and efficient detection mechanism, these replicas can be used to launch a variety of attacks that undermine many sensor applications and protocols. In this paper, we present a novel distributed approach called Localized Multicast for detecting node replication attacks. The efficiency and security of our approach are evaluated both theoretically and via simulation. Our results show that, compared to previous distributed approaches proposed by Parno et al., Localized Multicast is more efficient in terms of communication and memory costs in large-scale sensor networks, and at the same time achieves a higher probability of detecting node replicas.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it