SECURE ANONYMOUS COMMUNICATION FOR WIRELESS SENSOR NETWORKS BASED ON PAIRING OVER ELLIPTIC CURVES
Bibliographic record
Abstract
The nature of wireless communication makes it susceptible to a number of security threats, disclosing the identities of the communicating parties in the network. By revealing the identity of nodes in the network, outside parties can setup severe targeted attacks on specific nodes. Such targeted attacks are more harmful to sensor networks as sensing nodes (sensors) have limited computing and communication power prohibiting them from using robust security mechanisms. Anonymous communication is one of the key primitives for ensuring the privacy of communicating parties in a group or network. In this paper, we propose a novel secure anonymous communication protocol based on pairing over elliptic curves for wireless sensor networks (WSNs). Using this protocol, only the legitimate nodes in the sensor network can authenticate each other without disclosing their real identities. The proposed protocol is extremely efficient in terms of key storage space and communication overhead. Security analysis of our protocol shows that it provides complete anonymity for communicating nodes. The analysis also shows that the proposed protocol is robust against a number of attacks including the masquerade attack, wormhole attack, selective forwarding attack and message manipulation attack.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".