Research Article
Deep Autoencoders and Feedforward Networks Based on a New Regularization for Anomaly Detection
Table 3
Comparison of the proposed models’ results with existing approaches used on the UNSW-NB15 dataset.
| Model | No. of features | Classifier | Accuracy | FPR | TPR | Precision | F-score |
| Proposed FFN | 42 | DNN | 94.7 | 1.04 | 94.24 | 86.76 | 89.75 | Proposed VAE | 42 | Deep VAE | 93.3 | 0.93 | 95.21 | 87.9 | 90.2 | Two-stage ensemble [34] | — | Two-stage meta | 91.72 | 8.90 | 91.30 | 91.60 | — | GALR-DT [11] | 20 | DT | 81.42 | 6.39 | — | — | — | NAWIR [24] | 42 | AODE | 83.47 | 6.57 | 98.5 | — | — | [25] | 5 | RF | 81.61 | 4.40 | 81.6 | — | 79.5 | Standard MLP [37] | 42 | Softmax | 81.30 | 21.15 | — | — | — | TSDL [37] | 10 | Softmax | 89.13 | 0.74 | 63.27 | — | — | NB | 82.07 | 18.56 | | | | DT | 85.56 | 15.78 | | | | ANN | 81.34 | 21.13 | | | | LR | 83.15 | 18.48 | | | | [15] | 42 | EM | 78.47 | 23.79 | — | — | — |
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