Improving Intrusion Detection System Using Ensemble Methods and Over-Sampling Technique
Bibliographic record
Abstract
Due to the imbalanced training samples, anomaly-based intrusion detection system (IDS) has to face many problems such as a low detection accuracy, a high false alarm rate and insufficient application value, especially in multi-classification tasks. A variety of methods are proposed in this paper to improve the effect of IDS based on machine learning. Synthetic minority over-sampling technique (SMOTE) was used to alleviate the problems caused by sample imbalance and improve the model effect, through which new samples are generated by interpolation between K-nearest neighbor and minority. A combination method was used to improve the model effect and achieve the multi-classification of intrusion traffic in this paper. The experimental results show that the performance of IDS has improved in several metrics, which is 93.2% in Macro Average Precision, 98.9% in Macro Average Recall, 95.5% in Macro Average F1Score, and 99.4% in Macro Average AUC. CIC-IDS2017 public dataset provided by the Canadian Institute for Cybersecurity was used in this research. Code is available at: https://github.com/CSFanLi/IDS/tree/main/EnsembleLearning
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".