Research Article
N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection
Table 8
Description of detecting performance metrics of different deep learning architectures with different text sizes.
| Deep learning type number | Evaluation metrics | Text size (byte) of the transformed data from network flow | 200 | 500 | 700 | 1000 | 1200 | 1500 |
| 1 | Precision | 0.671 | 0.672 | 0.682 | 0.713 | 0.718 | 0.722 | Recall | 0.584 | 0.594 | 0.599 | 0.614 | 0.617 | 0.621 | F1-score | 0.625 | 0.631 | 0.638 | 0.660 | 0.664 | 0.667 | Accuracy | 0.625 | 0.633 | 0.640 | 0.658 | 0.661 | 0.665 |
| 2 | Precision | 0.711 | 0.761 | 0.827 | 0.843 | 0.869 | 0.878 | Recall | 0.624 | 0.669 | 0.694 | 0.727 | 0.749 | 0.757 | F1-score | 0.665 | 0.712 | 0.755 | 0.781 | 0.802 | 0.813 | Accuracy | 0.666 | 0.713 | 0.749 | 0.779 | 0.800 | 0.812 |
| 3 | Precision | 0.768 | 0.817 | 0.883 | 0.916 | 0.914 | 0.936 | Recall | 0.651 | 0.716 | 0.779 | 0.837 | 0.848 | 0.859 | F1-score | 0.705 | 0.763 | 0.828 | 0.874 | 0.879 | 0.896 | Accuracy | 0.701 | 0.764 | 0.829 | 0.878 | 0.883 | 0.899 |
| 4 | Precision | 0.767 | 0.816 | 0.883 | 0.915 | 0.908 | 0.934 | Recall | 0.651 | 0.718 | 0.777 | 0.833 | 0.847 | 0.853 | F1-score | 0.704 | 0.762 | 0.826 | 0.872 | 0.876 | 0.892 | Accuracy | 0.700 | 0.765 | 0.827 | 0.875 | 0.881 | 0.895 |
| 5 | Precision | 0.847 | 0.894 | 0.954 | 0.969 | 0.971 | 0.971 | Recall | 0.715 | 0.784 | 0.848 | 0.880 | 0.881 | 0.881 | F1-score | 0.775 | 0.836 | 0.898 | 0.922 | 0.924 | 0.924 | Accuracy | 0.772 | 0.836 | 0.899 | 0.924 | 0.925 | 0.926 |
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