Table 8 Comparative evaluation of GraphFedAI with recent DDoS detection approaches (2023–2025).
Model | Reference | Year | Learning method | Accuracy (%) | F1-score (%) | Scalability | Privacy preservation |
|---|---|---|---|---|---|---|---|
PortMap detection (CICDDoS2019) | 2024 | Machine Learning (RF, SVM) | 91.2 | 90.8 | Medium | No | |
KNN-neural network hybrid | 2024 | KNN + Neural Network | 93.5 | 93.0 | Low | No | |
RF-PCA-ANN hybrid | 2024 | Random Forest + PCA + ANN | 94.8 | 94.2 | Medium | No | |
Ensemble detection (IEEE ISCS) | 2024 | Ensemble Learning | 95.3 | 94.8 | Medium | No | |
Hybrid DL for DoS in SDN | 2023 | Hybrid Deep Learning (CNN + RNN) | 96.0 | 95.7 | High (SDN-specific) | No | |
Hybrid SAE + Checkpoint DL | 2023 | Stacked Autoencoder + Checkpoint Net | 95.4 | 94.9 | Medium | No | |
P3GNN (APT detection in SDN) | 2024 | Graph Neural Network | 96.8 | 96.2 | High | Partial | |
Federated learning against poisoning | 2024 | Robust Privacy-Preserving FL | 97.3 | 96.9 | High | Yes | |
AP2FL (Healthcare) | 2023 | Auditable Privacy-Preserving FL | 95.9 | 95.1 | Medium | Yes | |
FL-IDPP | 2025 | Federated Learning + DNN | 94.1 | 93.5 | Medium | Yes | |
PCA-CNN | 2024 | PCA + CNN | 91.6 | 91.0 | Low | No | |
GraphFedAI (Proposed) | Proposed | –- | Federated Learning + GNN + Interpolation | 98.7 | 98.4 | High (non-IID capable) | Yes |
