Research Article
Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets
Table 12
Constructing the baseline experiments with different testing modes.
| Test mode | ML algorithm | NSL-KDD | UNSW-NB15 | Accuracy | Precision | Recall | Accuracy | Precession | Recall |
| Supplied test set | 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 | Naïve Bayes | 76.1178 | 0.809 | 0.761 | 87.4435 | 0.884 | 0.874 |
| Percentage split | DT (J48) | 99.7245 | 0.997 | 0.997 | 100 | 1.000 | 1.000 | RF | 99.9019 | 0.999 | 0.999 | 100 | 1.000 | 1.000 | SVM | 97.4574 | 0.975 | 0.975 | 100 | 1.000 | 1.000 | ANN | 98.6085 | 0.986 | 0.986 | 100 | 1.000 | 1.000 | Naïve Bayes | 90.5933 | 0.907 | 0.906 | 92.1397 | 0.930 | 0.921 |
| Cross-validation | DT (J48) | 99.7817 | 0.998 | 0.998 | 100 | 1.000 | 1.000 | RF | 99.9174 | 0.999 | 0.999 | 100 | 1.000 | 1.000 | SVM | 97.405 | 0.974 | 0.974 | 100 | 1.000 | 1.000 | ANN | 98.5378 | 0.985 | 0.985 | 100 | 1.000 | 1.000 | Naïve Bayes | 90.3829 | 0.905 | 0.904 | 92.4809 | 0.932 | 0.925 |
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