Research Article
A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks
Table 6
Comparison of the proposed work with different models.
| Model | Dataset | Classifier | AC (%) | Precision | Recall | F1 score |
| Tang et al. [46] | NSL-KDD | SAAE-DNN | 87.74 | 86.47 | 84.12 | 85 | Wang et al. [5] | NSL-KDD | RNN | 94.19 | — | — | — | Al-Qatf et al. [6] | NSL-KDD | STL-IDS | 84.96 | 96.2 | 76.5 | 85.2 | Ingre et al. [7] | NSL-KDD | ANN | 81.2 | — | — | — | Tang et al. [8] | NSL-KDD | SDN-DNN | 75.75 | 83 | 75 | 74 | Yin et al. [9] | NSL-KDD | RNN-IDS | 83.28 | — | 97.09 | — | Li et al. [10] | NSL-KDD | GoogLeNet | 81.84 | 81.84 | 100 | 90.01 | Tama et al. [11] | NSL-KDD UNSW-NB15 | TSE-IDS | 85.79 | 88 | 86.80 | 87.4 | Choudhary et al. [12] | NSL-KDD | DNN | 91.7 | 93.6 | 92 | 92.2 | Farahnakian et al. [13] | KDD99 | DAE | 96.53 | — | — | — | YU et al. [34] | NSL-KDD UNSW-NB15 | CNN | 92.33 | 96.1 | 95 | 93 | Wang et al. [48] | NSL-KDD | SDAE-ELM1 | 78.04 | 95.99 | 64.12 | 76.87 | Proposed model | NSL-KDD | DNN | 99.73 | 99.75 | 99.73 | 99.72 |
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