FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection
Why this work is in the frame
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Bibliographic record
Abstract
Distributed Denial-of-Service (DDoS) attacks are a major threat to computer networks. These attacks can be carried out by flooding a network with malicious traffic, overwhelming its resources, and/or making it unavailable to legitimate users. Existing machine learning methods for DDoS attack detection typically use statistical features of network traffic, such as packet sizes and inter-arrival times. However, these methods often fail to capture the complex relationships between different traffic flows. This paper proposes a new DDoS attack detection approach that uses Graph Neural Networks (GNN) ensemble learning. GNN ensemble learning is a type of machine learning that combines multiple GNN models to improve the detection accuracy. We evaluated our approach on the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset (CICIDS2018) and CICIDS2017 datasets, a benchmark dataset for DDoS attack detection. Our work provides two main contributions. First, we extend our DDoS attack detection approach using GNN ensemble learning. Second, we explore the evaluation and fine-tuning of hyperparameter metrics through ensemble learning, significantly enhancing accuracy compared to a single GNN model and achieving an average 3.2% higher F1-score. Additionally, our approach effectively reduces overfitting by incorporating regularization techniques, such as dropout and early stopping. Specifically, we use a hierarchical ensemble of GNN, where each GNN learns the relationships between traffic flows at a different granularity level. We then use bagging and boosting to combine the predictions of the individual GNN, further improving detection accuracy. Results show that our system can achieve 99.67% accuracy, with a F1-score of 99.29%, which is better than state-of-the-art methods, even using single traffic architecture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it