Passphrase protected device‐to‐device mutual authentication schemes for smart homes
Bibliographic record
Abstract
Smart home is a promising paradigm and technology for providing the ability to physical objects to operate over the Internet. While this technology provides convenience to its users a number of initiatives across the globe are taken in academia and industry to deal with security issues that it may entail. Among these is the issue of interdevices authentication in the presence of security threats such as vulnerability of session/cookies. In this paper two passphrase protected device‐to‐device (D2D) mutual authentication schemes for smart homes are proposed where the keys are protected using passphrases and a centralized server provides proxy‐passphrase service to smart home devices assuming that the server keeps the database of passphrases as well as the servers' passphrase‐proxy service. The high‐level protocol specification language (HLPSL) language is used to model the proposed two protocols and a security analysis is conducted using the security protocol animator for AVISPA (SPAN)/AVISPA (Automated Validation of Internet Security Protocol and Applications) tool showing that the proposed schemes can achieve the goals of secrecy of secret keys and D2D mutual authentication
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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".