A RSA-Biometric Based User Authentication Scheme For Smart-Homes Using Smartphone
Bibliographic record
Abstract
Internet of Things (IoT) is considered as one of the emerging leading technologies that allow the mainstreaming of smart homes and smart cities in the recent years, by creating a communication system for physical objects over the Internet. In a smart home (also called push-button home automation system), devices are not necessarily homogeneous in terms of topology, security protocols, computational power and communication. This nature of the devices causes some incompatibility with conventional authentication methods and the security requirements of IoT standards. This thesis proposes an RSA-Biometric based three-factor User Authentication Scheme for Smart-Homes using Smartphone (called RSA-B-ASH-S scheme). An informal security analysis of the proposed RSA-B-ASH-S scheme is provided, along with its performance evaluation in terms of computational time, storage requirements and communication overload. Furthermore, a formal analysis of the proposed RSA-B-ASH-S scheme using the Burrows-Abadi-Needham (BAN) Logic is described, showing that the proposed scheme achieves the forward secrecy property by utilizing a fresh encryption key for each session and it also satisfies the anonymity of the user by using a one-time token. A proof of concept of the proposed RSA-B-ASH-S scheme is also provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".