Toward generating a DoS and scan statistical network traffic metrics for building intrusion detection solution based on machine and deep learning: I-Sec-IDS datasets
Bibliographic record
Abstract
In this work, we propose a Denial of Service (DoS) and scan statistical network traffic metrics datasets to build upon Intrusion Detection System (IDS) solutions based on Machine and Deep Learning (MDL) methodologies.We generate the datasets in VirtualBox environment.Two guests are involved and configured to perform the aforementioned attacks for collecting the network traffic.The first one is a Kali Linux VirtualBox machine that executes the DoS and scan attacks against the second one guest, a Microsoft 10.The host machine captures the exchanged network traffic between two guests via Wireshark and saves it in PCAP files.We extract the Canadian Institute of Cybersecurity (CIC)' FlowMeter metrics in Comma-separated Values (CSV) format to label our generated statistical network traffic metrics.Thus, this paper produces the DoS and scan statistical network traffic metrics datasets, cleansed up them before to be free available.In conclusion, this work aims to realise a first training datasets evaluation to design an IDS solutions, based on MDL techniques, upon our datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".