INSECS-DCS: A Highly Customizable Network Intrusion Dataset Creation Framework
Bibliographic record
Abstract
One critical challenge in design and operation of network intrusion detection systems (IDS) is the limited datasets used for IDS training and its impact on the system performance. If the training dataset is not updated or lacks necessary attributes, it will affect the performance of the IDS. To overcome this challenge, we propose a highly customizable software framework capable of generating labeled network intrusion datasets on demand. In addition to the capability to customize attributes, it accepts two modes of data input and output. One input method is to collect real-time data by running the software at a chosen network node and the other is to get Raw PCAP files from another data provider. The output can be either Raw PCAP with selected attributes per packet or a processed dataset with customized attributes related to both individual packet features and overall traffic behavior within a time window. The abilities of this software are compared with a product which has similar intentions and notable novelties and capabilities of the proposed system have been noted.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".