Table 13.
Comparative performance of APATCP against DL/RL-based and hybrid DDoS defense methods.
| Method | Approach Type | Accuracy (%) | F1-Score (%) | FPR (%) | Avg Response Time (s) | Computational Complexity |
|---|---|---|---|---|---|---|
| APATCP (Proposed) | P4 + Multi-controller + Adaptive ML | 99.14 | 99.01 | 1.01 | 1.13 | Low (data-plane offloading) |
| SAINT Transformer45 | Deep Transformer DL | 97.15 | 96.78 | 2.65 | 2.46 | High (attention overhead) |
| Dynamic Fog ML Framework46 | Fog + Entropy + ML | 96.42 | 96.05 | 2.96 | 3.07 | Medium (fog coordination cost) |
| DL + DRL SDN Framework41 | Deep Learning + Reinforcement Learning | 95.80 | 95.30 | 3.10 | 2.89 | High (training + policy learning) |
| CNN/DBN Detection Model43 | Deep Neural Networks | 96.10 | 95.60 | 2.90 | 2.49 | High (model inference cost) |
| Edge DL-Markov Model44 | Hybrid DL + Statistical RL-like adaptation | 95.40 | 95.00 | 3.25 | 3.70 | Medium |
| ONOS Flood Defender 48 | Statistical controller-based detection | 94.12 | 93.85 | 3.88 | 3.49 | Low |
| Classical ML Baseline 47 | RF/DT/LR | 92.80 | 92.11 | 4.07 | 3.68 | Low |