Bibliographic record
Abstract
Link layer security has been widely researched in the last decade as a means of protecting wireless networks (e.g., WEP, RSN, WPA, WPA2). However, there is little research in this area for Mobile Ad Hoc Networks (MANETs), especially for tactical MANETs. Although RSN can be used for MANETs as described in the IEEE 802.11i standard, it fails to meet some requirements of tactical MANETs, such as strong security, anonymity, and quick connectivity. In this paper, we propose a link layer anonymous access protocol (LAA) in order to provide strong security and anonymity protection for tactical MANETs. The protocol uses dynamic pseudonyms as network and node identities for network access authentication to prevent tracking, tracing, and other common attacks. It uses a localized key management mechanism for local shared key and broadcast key establishment that outperforms the connectivity and efficiency of key management in RSN and other link layer security technologies such as SEAMAN. Simulations show that LAA has only a small effect on end-to-end delay and no effect on packet delivery ratio relative to the standard MAC, meanwhile providing anonymous communication, better protection and improved connectivity performance in the link layer for tactical MANETs.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".