Network intrusion detection using machine learning approaches: Addressing data imbalance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Cybersecurity has become a significant issue. Machine learning algorithms are known to help identify cyberattacks such as network intrusion. However, common network intrusion datasets are negatively affected by class imbalance: the normal traffic behaviour constitutes most of the dataset, whereas intrusion traffic behaviour forms a significantly smaller portion. A comparative evaluation of the performance is conducted of several classical machine learning algorithms, as well as deep learning algorithms, on the well‐known National Security Lab Knowledge Discovery and Data Mining dataset for intrusion detection. More specifically, two variants of a fully connected neural network, one with an autoencoder and one without, have been implemented to compare their performance against seven classical machine learning algorithms. A voting classifier is also proposed to combine the decisions of these nine machine learning algorithms. All of the models are tested in combination with three different resampling techniques: oversampling, undersampling, and hybrid sampling. The details of the experiments conducted and an analysis of their results are then discussed.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it