Securing Domain Name System Combined with MIPv6 for Mobile Hosts
Bibliographic record
Abstract
DNS is the standard mechanism for name to IP address resolution. The DNS has been extended to DNSSEC to add security by providing origin authentication and data integrity by the process of creating signatures periodically, which results in intensive computations. Adding digital signatures to a domain increases each record size by 5-7 times, which puts a burden of DNS reply messages on the authoritative name servers. The goal of this paper is to find secure DNS mechanism, which cause relatively low computation loads and reply burden especially for infrastructure mode MANET gateways that are responsible for name resolution services as well as local mobility management for mobile hosts. This paper proposes SECDNS (Secure DNS) mechanism that handles secure query/reply transactions using the one-time session key generated per a query basis. In the proposed SECDNS, burden for securing DNS is distributed for every DNS queries. We analyze how many SECDNS transactions can the session key with a given length handle and suggest the solution of the anti-MITM attack scheme, which protects the name resolution services against the possible MITM attacks and make it useless for the enemy to decrypt the SECDNS reply messages in time.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".