Table 3 CICIDS2018: Proposed models
From: A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data
Authors | Proposed model(s) |
|---|---|
Atefinia and Ahmadi [14] | Modular Deep Neural Network |
Basnet et al. [15] | MLP |
Catillo et al. Â [16] | Deep Autoencoder |
Chadza et al. [17] | Baum Welch, Viterbi |
Chastikova and Sotnikov [18] | LSTM |
D’hooge et al. [19] | XGBoost |
Ferrag et al. [20] | RNN, Deep Autoencoder |
Filho et al. [21] | Random Forest |
Fitni and Ramli [22] | Logistic Regression + Decision Tree + Gradient Boosting |
Gamage and Samarabandu [23] | Deep Feed-forward Neural Network |
Hua [24] | LightGBM |
Huancayo Ramos et al. [25] | Random Forest, Decision Tree |
Kanimozhi and Jacob [26] | MLP |
Kanimozhi and Jacob [27] | MLP |
Karatas et al. [28] | Adaboost |
Kim et al. [29] | CNN |
Li et al. [30] | Deep Autoencoder |
Lin et al. [31] | LSTM |
Zhao et al. [32] | Deep Autoencoder |