Table 1 Literature review of IDS.
Author | Year | Dataset | Classification Model | Accuracy (%) | Technique Type |
|---|---|---|---|---|---|
Du et al.29 | 2023 | KDDCup99 | CNN - LSTM | 94.43 | Complex |
Chakrawarti & Shrivastava30 | 2023 | KDDCup99 | LSTM | 99 | Complex |
Wang et al.31, | 2023 | KDDCup99 | BIRCH + Auto Encoder | 99 | Complex |
Samunnisa et al.32, | 2023 | KDDCup99 | Kmeans, Gauss Mix, KNN, SVM | 97.21 | Complex |
Satyanarayana & Chatrapathi33 | 2023 | KDDCup99 | GA + Ensemble | 97.8 | Complex |
Jadhav et al.34, | 2023 | KDDCup99 | RNN + LSTM | 81.3 | Complex |
Alqarniv35 | 2023 | KDDCup99 | SVM + Ant colony optimization (ACO) | 95.5 | Complex |
Sharma et al.36, | 2023 | KDDCup99 | CNN | 99 | Medium |
Songma et al.37, | 2023 | CICSIDS2018 | PCA and RF | 98.7 | Complex |
Najafi & Tut38 | 2023 | CICSIDS2018 | Flower Pollination Algorithm and Artificial Bee Colony | 98.7 | Complex |
Alzughaibi & El Khediri39 | 2023 | CICSIDS2018 | MLP + BP and PSO | 96.25 | Complex |
Qazi et al.40, | 2023 | CICSIDS2018 | RNN + CNN | 98.9 | Complex |
Kshirsagar & Kumar41 | 2023 | CICSIDS2018 | Ensemble Feature Selection + DL | 99.9 | Complex |
Xu et al.42, | 2023 | CICSIDS2018 | CNN-BiLSTM-Attention model | 93.26 | Complex |
Li et al.43 | 2024 | CICSIDS2018 | CNN-LSTM and GAN | 98 | Complex |
