Using a Combination of Effective Feature Selection Methods and an Entropy-based Approach to Identify DDoS Anomalies
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) attacks are among the most dangerous types of attacks. These kinds of attacks bring targeted servers down and make their services unavailable to legal users. The first objective of this study is to identify infected Ethernet and detect various kinds of up-to-date DDoS attacks using a dynamic threshold by implementing multiple features of entropy and the Sequential Probabilities Ratio Test approach (E-SPRT). The second is to select relevant features to improve the performance of detection by implementing a new combination of machine learning techniques, which are ANOVA, Extra Trees Classifier, Random Forest, and Correlation Matrix with Pearson Correlation approaches. Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were utilised to evaluate the implementation. ESPRT using a feature selection approach with five features achieved an accuracy of over 97% with an average False Positive Rate (FPR) close to 0 in identifying most different kinds of DDoS attacks.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".