Evaluating the Efficacy of Resampling Techniques in Addressing Class Imbalance for Network Intrusion Detection Systems Using Support Vector Machines
Bibliographic record
Abstract
The objective of this study was to assess the performance of various resampling strategies aimed at mitigating the class imbalance problem in Network Intrusion Detection Systems (NIDS) using machine learning models and imbalanced benchmark datasets.Due to this class imbalance problem, detection of known or unknown attacks in NIDS often results in suboptimal performance.Resampling methods, statistically designed to generate synthetic samples from existing datasets, were employed to rebalance class labels and train the machine learning models.The Support Vector Machine (SVM), a robust supervised classifier, was utilized to classify data by identifying the optimal decision boundary that maximally separates different classes.In this context, efforts were made to enhance the effectiveness of these resampling techniques and consider the potential benefits of hybrid models.No resampling (NR), Synthetic Minority Over-sampling Technique (SMOTE), Random Under Sampling (RUS), Random Under Sampling and Random Over Sampling (RUS+ROS), and Random Under Sampling and SMOTE (RUS+SMOTE) were evaluated.The SVM classifier with Radial Basis Function (RBF) was employed, validated against the imbalanced benchmark dataset CICIDS-2017 (Canadian Institute for Cyber Security Intrusion Detection dataset-2017), to assess the effectiveness of these methods using performance metrics such as Accuracy, Precision, Recall, F1 score, and wall time.The proposed method achieved a remarkable accuracy of 99.63% in intrusion detection, demonstrating impressive results when compared to state-of-the-art methods for detecting network attacks on imbalanced datasets.The findings from this research provide valuable insights into the potential of various resampling methods in tackling class imbalance problems in NIDS.
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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 | 0.003 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".