Towards an Optimal Feature Selection Method for AI-Based DDoS Detection System
Bibliographic record
Abstract
Cyber-attacks are increasing rapidly, so developing effective intrusion detection and prevention tools for a secure and safer cyberspace is crucial. DDoS (Distributed Denial of Services) is one of the most well-known digital threats, endangering any cyber-physical system. DDoS prevents the host from serving the legitimate traffic by overflowing the host node with unwanted service requests. Nowadays, machine learning-based IDS (Intrusion Detection System) uses different Feature Selection (FS) methods to extract a feature subset from a large dataset to increase the model performance and decrease the training time. In this research work, we used the UNSW-NB15 dataset [1] to conduct a comprehensive analysis for evaluating the performance of different FS techniques in DDoS attack classification using both Machine Learning (ML) and Deep Learning (DL) models. Furthermore, an Ensemble Feature Selection (EN-FS) technique called Majority Voting (MV) has been implemented to combine the individual FS method’s output to extract an optimal feature set. Our ensemble feature selection approach significantly reduces the features from 42 to 15, which is 64% less than the original features. Lastly, an extensive experiment has been performed to estimate and compare the performance of individual, ensemble, and original feature set in both ML and DL-based DDoS detection systems. According to our analysis, the ensemble feature set-based classification model exhibits higher accuracy, lower False Positive Rate (FPR), and better execution time than the other individual feature set-based 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".