Boosting Intrusion Detection Against DDoS Attacks Using a Feature Engineering-Based Fine-Tuned XGBoost Model
Bibliographic record
Abstract
Network security is seriously threatened by distributed denial-of-service (DDoS) attacks, which calls for sophisticated intrusion detection systems that can rapidly identify and mitigate such threats. Despite their widespread use in intrusion detection against DDoS attacks, machine learning methods still suffer accuracy degradation due to inadequate data pre-processing and computational inefficiency. This study combined a fine-tuned extreme gradient boosting (XGBoost) model with correlation-based feature selection—for efficient feature selection—to effectively maximize detection accuracy while lowering computing overhead. Both correlation-based feature selection and XGBoost contribute to boosting the final model's efficiency. To evaluate the proposed model, different metrics were employed over three DDoS data sets, considering both binary and multi-classification scenarios. Experimental findings demonstrate that the proposed XGBoost achieves highly competitive accuracy. For the Network Security Laboratory–Knowledge Discovery Databases data set, University of New South Wales–Network Behavior15 data set, and Canadian Institute for Cybersecurity–Intrusion Detection System–2017 data set, the model secures 0.995, 1.000, and 0.999 and 0.996, 0.885, 0.998 for binary and multi-classification, respectively, outperforming its rival models.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".