Review Article
A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis
Table 3
Critical review of federated learning (FL) based approaches in ID.
| Ref | Authors | Year | Cited by | FL approach | Accuracy (%) |
| [80] | Supriya and Gadekallu | 2023 | 1 | FL-based approach particle swarm optimization (PSO) | 94.47 | [81] | Mu et al. | 2023 | 16 | FedProc: Prototypical contrastive FL | Improves accuracy by 1.6% to 7.9 | [82] | Yu et al. | 2023 | 1 | FL-based Iron forge approach | 97 | [83] | Nguyen et al. | 2020 | 78 | FL-based IoT IDS | 99.9 | [84] | Liu et al. | 2021 | 70 | FL and Blockchain based IDS | >80 | [85] | Chen et al. | 2020 | 54 | Federated learning-based attention gated recurrent unit (FedAGRU) | 98.82 | [86] | Rahman et al. | 2020 | 119 | FL-based scheme | 83.09 | [87] | Mothukuri et al. | 2021 | 173 | FL-based anomaly detection approach | 90.255 | [88] | Zhao et al. | 2019 | 94 | Multi-task deep neural network in federated learning (MT-DNN-FL) | 96.54 | [89] | Rey et al. | 2022 | 93 | FL-based malware detection framework | 98.59 | [90] | Belenguer et al. | 2022 | 6 | FL-based application | 92 | [91] | Zhang et al. | 2022 | 9 | SecFedNIDS: Robust defense for poisoning attack against federated learning-based network IDS | Improves 48 | [92] | Sarhan et al. | 2023 | 10 | Collaborative cyber threat intelligence sharing scheme | 92 |
|
|