Correction: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment
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
<p dir="ltr">Correction to: Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment <a href="https://dx.doi.org/10.1186/s13677-022-00356-9" target="_blank">https://dx.doi.org/10.1186/s13677-022-00356-9</a>, published online 23 January 2023. <p dir="ltr">Following publication of the original article [1], we have been notified that affiliation of Deema Mohammed alsekait is now: <p dir="ltr">6 Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, India <p dir="ltr">It should be: <p dir="ltr">6 Department of Computer Science and Information Technology, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia <p dir="ltr">The original article was updated. <h2>Other Information</h2><p dir="ltr">Published in: Journal of Cloud Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13677-023-00551-2" target="_blank">https://dx.doi.org/10.1186/s13677-023-00551-2</a>
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it