TCP/UDP-Based Exploitation DDoS Attacks Detection Using AI Classification Algorithms with Common Uncorrelated Feature Subset Selected by Pearson, Spearman and Kendall Correlation Methods
Bibliographic record
Abstract
The Distributed Denial of Service (DDoS) attack is a serious cyber security attack that attempts to disrupt the availability security principle of computer networks and information systems. It's critical to detect DDoS attacks quickly and accurately while using as less computing power as possible in order to minimize damage and cost efficient. This research proposes a fast and high-accuracy detection approach by using features selected by proposed method for Exploitation-based DDoS attacks. Experiments are carried out on the CICDDoS2019 datasets Syn flood, UDP flood, and UDP-Lag, as well as customized dataset. In addition, experiments were also conducted on a customized dataset that was constructed by combining three CICDDoS2019 datasets. Pearson, Spearman, and Kendall correlation techniques have been used for datasets to find un-correlated feature subsets. Then, among three un-correlated feature subsets, choose the common un-correlated features. On the datasets, classification techniques are applied to these common un-correlated features. This research used conventional classifiers Logistic regression, Decision tree, KNN, Naive Bayes, bagging classifier Random forest, boosting classifiers Ada boost, Gradient boost, and neural network-based classifier Multilayer perceptron. The performance of these classification algorithms was also evaluated in terms of accuracy, precision, recall, F1-score, specificity, log loss, execution time, and K-fold cross-validation. Finally, classification techniques were tested on a customized dataset with common features that were common in all of the dataset’s common un-correlated feature sets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.000 | 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".