Bibliographic record
Abstract
The primary requirements of a secure Wireless Sensor Network architecture are confidentiality, integrity and authentication of users and other participating entities. User Authentication for wireless sensor networks is a fundamental and important issue in designing dependable and secure systems. In this thesis, we have outlined the security model, functional requirements, assumptions and network setup for an authentication scheme in the first phase. Keeping in mind the security requirements as well as the flaws of past authentication schemes, we propose a robust user authentication method that inherits user anonymity, mutual authentication and password changing functionality of previous password-based schemes and improves security by resisting gateway bypass and replay attack, and many logged in user with the same ID threat. Our scheme is a variant of strong password based schemes that does not require strict network synchronization. In the second phase of the thesis, we have analysed our authentication scheme from the perspective of security issues and functional requirements. The proposed scheme is modelled in SystemC. It is evaluated in different attack scenarios. The authentication latency, memory and functional requirements, and computational overhead are the metrics used to evaluate the scheme. The effect of multiple users on authentication latency in our scheme is also studied. Some of the past representative schemes have also been modelled and evaluated in the same environment. A detailed comparison of over-head cost, authentication latency and security features are provided in this thesis. It is verified and confirmed by modeling that our scheme provides enhanced security without adding extra computation at the sensor node.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".