For Robust DDoS Attack Detection by IDS: Smart Feature Selection and Data Imbalance Management Strategies
Bibliographic record
Abstract
Computer network security represents a major challenge in the digital age, where intrusions threaten data confidentiality, accuracy and accessibility.To safeguard data and online services, Intrusion Detection Systems (IDS) controls the network traffic for any signs of malicious activity.The integration of artificial intelligence into IDSs offers new perspectives, but poses challenges, particularly in terms of feature selection and data imbalance management.Our research focused on identifying DDoS attacks, a major threat to the accessibility of online services.We evaluated the effectiveness of IDS against these attacks by testing the RF, XGB, SGD, LGB and MLP machine learning models on the CICIDS2018 DDOS attacks dataset.To optimize data quality, we adopted a strategic feature selection approach based on correlation matrix, mutual information and feature importance, reducing data dimensionality and improving model performance.Then, by balancing our dataset using oversampling techniques such as SMOTE, BorderlineSMOTE and ADASYN, we achieved better model generalization and reduced false positives.Our results showed that the ADASYN+SMOTE+XGB configuration was the most optimal for DDoS attack detection regarding effectiveness, false positives and execution duration.Our approach, combining judicious feature selection and resampling, has enabled us to create more performing intrusion detection systems, strengthening network security against increasingly sophisticated threats.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".