Cryptographic key management methods for mission-critical wireless networks
Bibliographic record
Abstract
When a large scale disaster strikes, it demands an efficient communication and coordination among first responders to save life and other community resources. Normally, the traditional communication infrastructures such as landline phone or cellular networks are damaged and dont provide adequate communication services to first responders for exchanging emergency related information. Wireless mesh networks is the promising alternatives in such type of situations. The security requirements for emergency response communications include privacy, data integrity, authentication, access control and availability. To build a secure communication system, usually the first attempt is to employ cryptographic keys. In critical-mission wireless mesh networks, a mesh router needs to maintain secure data communication with its neighboring mesh routers. The effective designs on fast pairwise key generation and rekeying for mesh routers are critical for emergency response and are essential to protect unicast traffic. In this paper, we present a security-enhanced session key generation and rekeying protocols EHPFS (enhanced 4-way handshake with PFS support). It eliminate the DoS attack problem of the 4-way handshake in 802.11s. EHPFS provides additional support for perfect forward secrecy (PFS). Even in case a Primary Master Key (PMK) is exposed, the session key PTK will not be compromised. The performance and security analysis show that EHPFS is efficient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".