Addressing Class Imbalance in Intrusion Detection: A Comprehensive Evaluation of Machine Learning Approaches
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
The ever-growing number of cyber attacks in today’s digitally interconnected world requires highly efficient intrusion detection systems (IDSs), which accurately identify both frequent and rare network intrusions. One of the most important challenges in IDSs is the class imbalance problem of network traffic flow data, where benign traffic flow significantly outweighs attack instances. This directly affects the ability of machine learning models to identify minority class threats. This paper is intended to evaluate various machine learning algorithms under different levels of class imbalances, using resampling as a strategy for this problem. The paper will provide an experimental comparison by combining various algorithms for classification and class imbalance learning, assessing the performance through the F1-score and geometric mean (G-mean). The work will contribute to creating robust and adaptive IDS through the judicious integration of resampling with machine learning models, thus helping the domain of cybersecurity to become resilient.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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