Deep Graph Learning for DDoS Detection and Multi-Class Classification IDS
Bibliographic record
Abstract
Critical infrastructure systems have been preyed on by cyber criminals that target to disrupt their operations and national security. Among the most nefarious attacks, the Distributed Denial of Service (DDoS) attack is wreaking havoc on the Telecommunications sector. This paper invests in the vision that Artificial Intelligence (AI) plays an important role in shoring up the cybersecurity of critical infrastructure providers by detecting and classifying malicious engagements. In this respect, we propose an efficient and dependable DDoS specialized intrusion section system (IDS). The proposed system is empowered by Graph Convolutional Networks (GCN), a deep learning technique, which is capable of capturing the topological and statistical information between the attack network and the victim network. The results show that the proposed GCN IDS can detect and classify multiple variations of DoS with a high confidence level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".