Research Article
ForChaos: Real Time Application DDoS Detection Using Forecasting and Chaos Theory in Smart Home IoT Network
Table 8
Machine learning algorithms results against application layer DDoS attacks IoT dataset.
| | Alg (Dataset) | DR(%) | TP(%) | FP(%) | FN(%) | Prec.(%) |
| | BN(1) | 95.6 | 86.7 | 1.6 | 13.3 | 94.5 |
| | NB(1) | 92.4 | 85 | 5.2 | 15 | 83.6 |
| | SVM(1) | 95.2 | 76.7 | 0 | 13.3 | 100 |
| | MLP(1) | 98.4 | 96.7 | 1 | 3.2 | 96.7 |
| | DT(1) | 98.8 | 96.7 | 0.5 | 3.2 | 98.3 |
| | RF(1) | 98.8 | 96.7 | 0.6 | 3.2 | 98.3 |
| | BN(2) | 92.8 | 80.7 | 0 | 19.3 | 100 |
| | NB(2) | 87.6 | 76.7 | 5.4 | 13.3 | 90.2 |
| | SVM(2) | 87.6 | 68.3 | 0 | 23.7 | 100 |
| | MLP(2) | 96.7 | 95 | 2.2 | 5 | 96.6 |
| | DT(2) | 90.2 | 86.7 | 7.5 | 13.3 | 88.1 |
| | RF(2) | 94.1 | 88.3 | 2.2 | 11.7 | 96.4 |
| | ForChaos | 94.3 | 87.5 | 5.34 | 12.5 | 81.82 |
|
|
