SCVIC-CIDS-2022: Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection
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
SCVIC-CIDS-2021 was created using the raw data in CIC-IDS-2018*, while SCVIC-CIDS-2022 is formed from NDSec-1** meta-data by following the similar procedure in Section *Sharafaldin, I.; Habibi Lashkari, A. and Ghorbani, A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy - ICISSP, ISBN 978-989-758-282-0; ISSN 2184-4356, pages 108-116. DOI: 10.5220/0006639801080116**Beer, F., Hofer, T., Karimi, D. & Bühler, U., (2017). A new Attack Composition for Network Security. In: Müller, P., Neumair, B., Raiser, H. & Dreo Rodosek, G. (Hrsg.), 10. DFN-Forum Kommunikationstechnologien. Bonn: Gesellschaft für Informatik e.V.. (S. 11-20).
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it