SCVIC-CIDS-2022: Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection
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).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.013 |
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".