Research Article
Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets
Table 13
Results after conducting the noise filtering.
| Noise filtering | ML algorithm | NSL-KDD | UNSW-NB15 | Accuracy | Precision | Recall | Accuracy | Precision | Recall |
| Baseline | DT (J48) | 81.5339 | 0.858 | 0.815 | 100 | 1.000 | 1.000 | RF | 80.4516 | 0.852 | 0.805 | 98.4903 | 0.985 | 0.985 | SVM | 75.3948 | 0.802 | 0.754 | 100 | 1.000 | 1.000 | ANN | 77.7147 | 0.817 | 0.777 | 100 | 1.000 | 1.000 | NB | 76.1178 | 0.809 | 0.761 | 87.4435 | 0.884 | 0.874 |
| Filtered | DT (J48) | 80.7116 | 0.844 | 0.807 | 100 | 1.000 | 1.000 | RF | 80.6742 | 0.844 | 0.807 | 99.9396 | 0.999 | 0.999 | SVM | 80.6367 | 0.796 | 0.806 | 100 | 1.000 | 1.000 | ANN | 80.6367 | 0.796 | 0.806 | 100 | 1.000 | 1.000 | NB | 80.4869 | 0.746 | 0.805 | 75.4608 | 0.800 | 0.755 |
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