Detection of TCP-Based DDoS Attacks with SVM Classification with Different Kernel Functions Using Common Uncorrelated Feature Subsets
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) is a server-side infrastructure type security attack that aims to prevent legitimate users from accessing server system resources. Huge financial losses, reputation damage and data theft are some of the serious circumstances of DDoS attacks. Available DDoS attack detection methods reduce the severity of the attack's consequences, but they require more data computation, which is more expensive. This research proposed two feature selection methods in order to reduce the data computation for TCP-based DDoS attack detection with Support Vector Machine (SVM) classification algorithm. The first feature selection proposal of this study is to use Pearson, Spearman, and Kendall correlation approaches to select the PSK common uncorrelated feature subset. Use these PSK common uncorrelated feature subsets with SVM classifier with different kernels on TCP-based DDoS attacks and evaluate the classification results. This research, performed operations on Syn flood, MSSQL, SSDP datasets have taken from the CIC-DDoS2019 evaluation dataset. Select TCP-based DDoS attacks common uncorrelated feature subset selected by applying intersection on Syn flood, MSSQL, and SSDP data sets PSK common uncorrelated feature subsets is the second feature selection proposal of this research. Use these TCP-based DDoS attacks common uncorrelated feature subsets with SVM classifier with different kernels on TCP-based DDoS attacks and evaluate the classification results. Results with these two proposed methods also compared in this study. Experiments have been performed with these two approaches on a customized TCP-based DDoS attack that's been developed with Syn flood, MSSQL, and SSDP data sets, and the results have been evaluated. Linear, rbf, poly, sigmoid kernels SVM kernels used in this research. Experiments conclude that SVM with rbf kernel produces better results on TCP-based 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".