# A Survey of CNN-Based Network Intrusion Detection
## 5 CNN-Based IDS Approaches  
This section provides an in-depth look at the CNN-based IDS schemes proposed in the literature. The reviewed approaches are categorized into four groups, with the first involving approaches that employ only a CNN. Furthermore, group 2 contains IDS schemes that combine a CNN with an RNN. To do this, an RNN method such as long short-term memory (LSTM) or gated recurrent unit (GRU) must be employed. In this group, the temporal features are extracted through RNN algorithms. Meanwhile, group 3 contains IDS approaches that combine CNN and deep learning methods other than RNNs. Finally, group 4 includes IDS approaches that combine CNN with machine learning, fuzzy, Fourier transformation, clustering, or evolutionary algorithms. The key features of CNN-based IDS approaches are outlined in Tables 5-12. The tables present essential information, including \"the year of publication\", \"architecture\", \"the simulators/environments (ENV)\", \"input layer shape\", \"datasets that are used for training and evaluation\", \"performance (accuracy)\", \"feature extraction method\", \"classification method\", \"performance metrics\", and \"other methods used alongside the CNN.\" In all of the tables below (Tables 5, 7, 9, and 11), we use the notations in the ENV column, which are KT: Keras using the TensorFlow backend; KH: Keras using the Theano backend; T: TensorFlow; M: MATLAB; S: Scikit-learn; J: Java with Deeplearning4j; W: WEKA; and P: PyTorch. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Architecture** & **Input Shape** & **Feature Extraction** & **Classifier** \\ \hline [PERSON] et al. [16] & 3 Conv, 2 MaxPool, 1 FC & 2D (8 \(\times\) 8) & CNN & SoftMax \\ [PERSON] et al. [18] & 3 Conv, 3 MaxPool, 2 FC & 2D (11 \(\times\) 11) & CNN & SoftMax \\ [PERSON] et al. [17] & 3 Conv, 2 MaxPool, 2 FC & 2D (32 \(\times\) 32) & CNN & SoftMax \\ [PERSON] et al. [19] & 2 Conv, 2 MaxPool, 1 FC & 1D (32) & CNN & SoftMax \\ [PERSON] et al. [20] & 5 Conv, 2 MaxPool, 4 FC & 2D (11 \(\times\) 14) & CNN & SoftMax \\ [PERSON] et al. [21] & 3 Conv, 2 MaxPool, 2 FC & 1D & CNN & SoftMax \\ [PERSON] et al. [22] & 1 Conv, 1 MaxPool,1 FC & 2D (28 \(\times\) 28) & \
\begin{tabular}{c} CNN, weighted \& \\ compressed \\ \end{tabular} & SoftMax \\ [PERSON] et al. [23] & 3 Conv, 2 MaxPool & 2D (13 \(\times\) 9), & CNN & SoftMax \\ [PERSON] et al. [24] & 4 Conv, 2 MaxPool, 1 Dropout, 2 FC & 2D (32 \(\times\) 32) & CNN & SoftMax \\ [PERSON] et al. [25] & 2 Conv, 2 MaxPool, 3 FC & 2D (9 \(\times\) 10) & CNN & SoftMax \\ [PERSON] et al. [26] & 2 Conv, 3 FC & 2D (7 \(\times\) 7) & CNN & SoftMax \\ [PERSON] et al. [27] & 2 Conv, 2 MaxPool, 1 FC & 2D (32 \(\times\) 32) & CNN & SoftMax \\ [PERSON] et al. [28] & 3 MSConv, 2 Conv,2 AvgPool, 3 FC & 2D (11 \(\times\) 11) & CNN & SoftMax \\ [PERSON] et al. [111] & 3 Conv, 3 MaxPool,3 FC & 1D (122) & CNN & SoftMax \\ [PERSON] et al. [29] & 2 Conv, 2 MaxPool, 1 FC & 2D (11 \(\times\) 11) & CNN & SoftMax \\ [PERSON] et al. [30] & 2 Conv, 1 MaxPool, 2 FC & 2D (6 \(\times\) 6) & CNN & SoftMax \\ [PERSON] et al. [31] & 5 Conv, 2 MaxPool, 4 FC & 1D (122) & CNN & SoftMax \\ [PERSON] et al. [32] & 3 Conv, 3 MaxPool, 1 FC & 1D (84) & CNN & SoftMax \\ [PERSON] et al. [33] & 2 Conv, 2 MaxPool, 3 FC & 2D (11 \(\times\) 11) & CNN & SoftMax \\ [PERSON] et al. [34] & 2 Conv, 1 MaxPool, 1 BatchNo, 2 FC, 1 Dropout & 1D & transformation and & SoftMax \\ [PERSON] et al. [35] & 2 Conv, 1 MaxPool, 1 Flatten, 2 FC & 1D (157) &
\begin{tabular}{c} Minimum redundancy \\ Maximum relevance \\ \end{tabular} & SoftMax \\ [PERSON] et al. [36] & 2 Conv, 1 FC & 2D & ADASYN, feature fusion & SoftMax \\ [PERSON] et al. [37] & 3 Conv, 3 MaxPool, 1 Flatten, 2 Fc & 1D & IG, CNN & SoftMax \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Architectural properties of group 1 (Single CNN-Based) IDS schemes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline