Review Article
A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis
Table 1
Critical review of machine learning (ML) based approaches in ID.
| Ref | Authors | Year | Cited by | ML approach | Accuracy (%) |
| [33] | Ahmed et al. | 2022 | 14 | Random forest (RF) | 95.1 | [34] | Singh et al. | 2022 | 15 | Support vector Regression | 98 | [35] | Pranto et al. | 2022 | 9 | ML-based ensemble feature selection strategy | 99.5 | [36] | Raghuvanshi et al. | 2022 | 48 | SVM | 98 | [37] | Albulayhi et al. | 2022 | 28 | ML-based IDS | 99.98 | [38] | Asif et al. | 2021 | 79 | ML-based method tangled with the MapReduce-Based intelligent model for ID (MR-IMID) | 97.7 | [39] | Çavuşoğlu | 2019 | 112 | Hybrid and layered IDS | 99.7 | [40] | Alqahtani et al. | 2020 | 82 | RF | 94 | [41] | Liu and Lang | 2019 | 457 | KNN | 99 | [42] | Ren et al. | 2019 | 80 | IDS by using hybrid data optimization (DO-IDS) | 92.8 | [43] | Bindra and Sood | 2019 | 53 | RF | 96 | [44] | Sai Kiran et al. | 2020 | 43 | SVM | 98.95 | [45] | Saranya et al. | 2020 | 127 | RF | 99.81 | [46] | Logeswari et al. | 2023 | 5 | Hybrid feature selection (HFS-light GBM IDS) | 98.72 | [47] | Muhammad and Saleem | 2022 | 39 | Naïve Bayes | 98.6 |
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