Enhancing broadcast authentication in sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the nature of wireless sensor networks, security is a critical problem since resource constrained and usually unattended sensors are much vulnerable to malicious attackers that may impersonate the sender. Therefore authenticating received messages is a crucial matter to protect the system integrity. Generally used TESLA (Timed Efficient Stream Loss-tolerant Authentication) based authentication techniques involve consecutive delays for decryption purposes. These delays render the network vulnerable to different malicious attacks such as Denial of Service attack. As several techniques try to achieve immediate authentication to alleviate these threats, other factors such as reliability and buffer requirements may have been compromised. This project proposes an integration of Low Buffer ,uTESLA protocol and an immediate authentication protocol to achieve a new refined scheme in broadcast authentication in sensor networks. Performance analysis and simulation results demonstrate that the proposed method succeeds to achieve immediate authentication while preserving desired security and low memory requirements in sensor nodes.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 it