Bootstrapping Security in Mobile Ad Hoc Networks Using Identity-Based Schemes with Key Revocation
Bibliographic record
Abstract
In this work, we introduce two full functional identity-based authentication and key exchange (IDAKE) schemes for mobile ad hoc networks (MANETs). Therefore, we utilize some special features of identity-based cryptographic (IBC) schemes, such as pre-shared secret keys from pairings and efficient key management, to design MANET-IDAKE schemes that meet the special constraints and requirements of MANETs. As part of these schemes, we present the first key revocation and key renewing algorithms for IBC schemes. The former algorithm uses a new concept of neighborhood watch. We introduce a basic MANET-IDAKE scheme in which a trusted third party (TTP) initializes all devices before they join the network and a fully self-organized MANET-IDAKE scheme that does not require any central TTP. The schemes bootstrap the security in MANETs and enable the use of authentication, key exchange, and other security protocols in a variety of applications. Furthermore, we present an extremely efficient yet secure IDAKE protocol that can be used in the presented schemes. Finally, we provide a security and performance discussion of the presented MANET-IDAKE schemes and IDAKE protocol.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".