Enabling semi-supervised learning in intrusion detection systems
Bibliographic record
Abstract
Intrusion Detection systems (IDS) are alerting cybersecurity tools that analyze network traffic in order to identify suspicious activity and known threats. State of the art IDS rely on supervised machine learning models which are trained to categorize the network flow with a historical labeled dataset. Nonetheless, next-generation networks are characterized as heterogeneous and dynamic. The heterogeneity can make every network environment to be significantly different and the dynamicity means that new threats are constantly emerging. These two factors raise the research question if a supervised machine learning based IDS can work efficiently in a network environment different from the one that generated its labeled training data. In this paper, we first give an answer to this research question and next try to propose a semi-supervised learning approach that can be generalized sufficiently in a different network environment using unlabeled data, taking into consideration that unlabeled data are much easier and cheap to be collected compared to labeled ones. In order to have a proof of concept we made experiments with two labeled datasets CIC-IDS2017, CIC-IDS2018 which are publicly available and one unlabeled dataset PS-Azure2023 which we constructed for this work and make it also publicly available. The results confirm our assumption and the applicability of the semi-supervised learning paradigm for the design of IDS.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".