Effective IDS under constraints of modern enterprise networks: revisiting the OpTC dataset
Bibliographic record
Abstract
The lack of high-quality public datasets is a major obstacle for the creation of practical and effective intrusion detection systems. Hundreds of new publications dedicated to this topic are released every year, but the majority of researchers have to rely on industry partners to get access to proprietary datasets, or use a handful of “traditional” synthetic datasets, such as KDD99 or CIC-IDS2017. Due to their age and simplicity of the simulated attacks, these datasets cannot contribute to the detection of intrusions in modern environments. At the same time, newer, advanced datasets, such as OpTC, have been largely ignored by the scientific community. Due to the enormous volume and almost complete lack of documentation, only few authors were able to leverage it for the evaluation of their IDS designs. In this work, we aim to make the OpTC dataset more accessible for modern IDS research by pointing out methodology flaws in prior art, designing a repeatable and documented labeling process, and building the baseline of the environment.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".