Towards Effective Network Intrusion Detection in Imbalanced Datasets: A Hierarchical Approach
Bibliographic record
Abstract
The transition from conventional networks to Soft-ware Defined Networks (SDNs) has revolutionized network man-agement and control, but it also creates a huge security risk, underscoring the significance of effective intrusion detection systems (IDS). Researchers have used deep learning for IDS due to its ability to capture complex patterns in data. Deep Learning techniques rely on ample balanced labeled data for effective intrusion detection, but acquiring such balanced data in real network scenarios is a formidable challenge, often resulting in suboptimal performance for existing methods when dealing with imbalanced datasets. This paper introduces a hierarchical approach that is capable of effectively detecting well-known network attacks in an SDN environment, even with minimal training data. Our model, leveraging a dataset collected from a real-world software-defined wide area network (SD-WAN) environment, showcases remarkable adaptability by maintaining strong performance even with highly imbalanced data, i.e., attack samples with as few as 8 or 16 instances to others with hundreds, thousands, or even millions of instances. It consistently achieves an overall average F1 score above 92%, with minority class average F1 score reaching more than 84%, marking a substantial 22.50% performance improvement compared to selected base-lines in our evaluation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".