A Novel DDoS Detection and Multi-Class Classification Method: A Graph Convolutional Network Approach
Bibliographic record
Abstract
Distributed Denial of Service (DDoS) is an attack that overwhelms the cyber critical infrastructure system with malicious packets causing it to become unresponsive, which precludes legitimate users from accessing the target system. This work leverages a deep learning method known as Graph Convolutional Network (GCN) to empower DDoS detection systems. The proposed GCN model consists of three hidden layers, each with 128 neurons. Considering the Canadian Institute for Cybersecurity CIC-IDS 2017 dataset, the proposed model achieves an overall accuracy of 99.95%, along with a value of 99.95% for each of the precision, recall, and F1-score metrics for the binary DDoS classification problem. For the multi-class DDoS classification problem, the model scores an overall accuracy of 98.94% and precision, recall, and F1-score values of over 93% for all classes. These results support the use of the proposed GCN DDoS detection method in practice.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".