Enhancing IDS performance through a comparative analysis of Random Forest, XGBoost, and Deep Neural Networks
Bibliographic record
Abstract
Intrusion Detection Systems (IDS) face major challenges in network security, notably the need to combine a high detection rate with reliable performance. This reliability is often affected by class imbalances and inadequate hyperparameter optimization. This article addresses the issue of improving the detection rate of IDS by evaluating and comparing three machine learning algorithms: Random Forest (RF), XGBoost, and Deep Neural Networks (DNN), using the NSL-KDD dataset. In our methodology, we integrate SMOTE (Synthetic Minority Oversampling Technique) to tackle the unbalanced nature of the data, ensuring a more balanced representation of the different classes. This approach helps optimize model performance, reduce bias, and enhance robustness. Additionally, hyperparameter optimization is performed using Optuna, ensuring that each algorithm operates at its optimal level. The results show that our model, using the Random Forest algorithm, achieves an accuracy of 99.80%, surpassing the performance of XGBoost and Deep Neural Networks (DNN). This makes our approach a true asset for intrusion detection methods in computer networks. • Random Forest achieved 99.80% accuracy and 0.9988 AUC on the NSL-KDD dataset. • Comparative performance evaluation of RF, XGBoost, and DNN for IDS. • Data imbalance in IDS addressed using the SMOTE technique. • The proposed approach outperforms the most comparable IDS models in detection accuracy. • IDS framework adaptable to various network datasets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".