Research Article
A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks
Table 3
Comparison of different feature selection techniques with the proposed feature selection model.
| Method | Feature selection | Dataset | Features used | Accuracy |
| Ahmadi et al. [36] | Chi squared, information gain, correlation-based evaluation | NSL-KDD | 20 | 80.6 | Liu et al. [37] | ANOVA | NSL-KDD, UNSWNB15, CICIDS-2017 | 16, 13, 39 | 83.28 | Gottwalt et al. [39] | CorrCorr | NSL-KDD, UNSWNB15 | 19 | 95 | Vinutha et al. [40] | Chi squared, information gain, gain ratio, correlation-based attribute evaluation, symmetrical uncertainty | NSL-KDD | 31 | 85.91 | Tang et al. [41] | SDN environment-based six basic features | NSL-KDD | 6 | 75.75 | Bhattacharya et al. [42] | Layered wrapper feature selection approach | NSL-KDD | 16 | 83.14 | Rama et al. [43] | Hyper graph-based genetic algorithm (HG-GA) | NSL-KDD, KDD-99 | 35 | 97.14 | Mohammadi et al. [44] | Linear correlation, cuttlefish algorithm, decision tree | KDD99 | 10 | 95.03 | Gao et al. [45] | CART algorithm | NSL-KDD | 17 | 79.7 | Tang et al. [46] | Stacked autoencoder | NSL-KDD | Autoselection | 87.74 | Proposed model | ANOVA, chi square, PCA | NSL-KDD | 27 | 99.73 |
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