Evaluation of SVM Kernel Functions to Detect DDoS attacks
Bibliographic record
Abstract
The digital landscape undergoes a constant transformation with the rise of novel technologies. As novel advancements emerge, cyberattacks become more commonplace and cunning, exploiting these very innovations. A Distributed Denial of Service (DDoS) attack is a type of cyber-attack that aims to compromise the availability of information security, thereby disrupting services for legitimate users. Detecting DDoS attacks is essential to lessen their impact. This paper introduces a method for detecting DDoS attacks using network flow features, as opposed to the more commonly utilized network type features. The suggested method utilizes the Support Vector Machine (SVM) classification algorithm, employing different kernel functions such as linear, RBF, polynomial, and sigmoid. To identify uncorrelated feature subsets, Pearson, Spearman, and Kendall correlation methods were utilized. Experiments were conducted using the CIC-DDoS2019 dataset from the Canadian Institute for Cyber Security. The study found that using the uncorrelated feature subset identified by Pearson's method resulted in superior performance with SVM's RBF and polynomial kernel functions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.001 | 0.001 |
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".