Research Article
Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets
Table 16
The results of running ML algorithms on an injected and then filtered intrusion dataset.
| Noise (%) | 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 |
| 5% noise injected then filtering | DT (J48) | 77.9026 | 0.828 | 0.779 | 100 | 1.000 | 1.000 | RF | 77.8277 | 0.749 | 0.778 | 97.051 | 0.973 | 0.971 | SVM | 77.6779 | 0.715 | 0.777 | 100 | 1.000 | 1.000 | ANN | 77.8652 | 0.828 | 0.779 | 100 | 1.000 | 1.000 | NB | 77.6779 | 0.715 | 0.777 | 74.2945 | 0.795 | 0.743 |
| 10% noise injected then filtering | DT (J48) | 73.97 | 0.742 | 0.740 | 100 | 1.000 | 1.000 | RF | 73.8577 | 0.682 | 0.739 | 95.0076 | 0.957 | 0.950 | SVM | 73.8202 | 0.676 | 0.738 | 100 | 1.000 | 1.000 | ANN | 74.0824 | 0.779 | 0.741 | 100 | 1.000 | 1.000 | NB | 73.8202 | 0.676 | 0.738 | 74.2055 | 0.795 | 0.742 |
| 20% noise injected then filtering | DT (J48) | 75.2434 | 0.762 | 0.752 | 73.392 | 0.849 | 0.734 | RF | 66.7416 | 0.589 | 0.667 | 91.5565 | 0.929 | 0.916 | SVM | 67.7154 | 0.620 | 0.677 | 100 | 1.000 | 1.000 | ANN | 67.6404 | 0.539 | 0.676 | 100 | 1.000 | 1.000 | NB | 67.7154 | 0.620 | 0.677 | 74.0753 | 0.794 | 0.741 |
| 30% noise injected then filtering | DT (J48) | 66.6292 | 0.676 | 0.666 | 70.2269 | 0.788 | 0.702 | RF | 61.985 | 0.595 | 0.620 | 88.6869 | 0.900 | 0.887 | SVM | 61.7228 | 0.573 | 0.617 | 99.9873 | 1.000 | 1.000 | ANN | 61.7978 | 0.621 | 0.618 | 99.8062 | 0.998 | 0.998 | NB | 61.7228 | 0.573 | 0.617 | 74.088 | 0.794 | 0.741 |
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