Securing wireless sensor networks against large-scale node capture attacks
Bibliographic record
Abstract
Securing wireless sensor networks against node capture is a challenging task. All well-known random key pre-distribution systems, including the Eschenauer and Gligor's pioneering scheme, its extensions, as well as threshold schemes, become insecure when a large number of nodes are captured. We propose a general technique, called virtual key ring, that can effectively strengthen the resilience of random key pre-distribution systems against node capture attacks by reducing the pre-loaded keying material while maintaining secure connectivity of the network.The technique is general and applicable to many key pre-distribution systems. We however focus on the original EG scheme and propose a virtual key ring system based on this pioneering scheme. We provide detailed mathematical analysis and a security proof for the system, and use extensive simulation to validate the analysis and to compare performance of the new system with the original EG scheme. We also present simulation results for the strengthened resilience when the virtual key ring scheme is combined with the multipath key reinforcement and q-composite techniques, showing that the system resilience is substantially improved against large-scale node capture attack (e.g., 40% of nodes captured).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".