## 5 Experimental Results and Discussion  
Deep learning is considered one of the latest achievements in machine learning. It helped the researchers to develop the solutions that could only be assumed a decade before because of the unavailability of data management, and data processing resources. In this research, we use 1D-CNN for the detection of network intrusion. 1D-CNN is designed specifically to operate in 1-dimensional data. Previously various generic and machine learning-based approaches were used by researchers for the detection of network intrusions. Table 4 shows class wise instances after preprocessing and Table 7 depicts the number of instances used for training and testing. We divided the dataset into two sets, i.e., 75 and 25% for training and testing purposes, respectively. We evaluated the performance of the 1D-CNN classifier using different performance metrics, e.g., accuracy, precision, recall, and F1 score. These metrics are explained in Equations (3)-(6), respectively. The results of the CNN classifier are populated in Table 8.  
\[Accuracy=\frac{TP+TN}{Total\ Samples} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[F1\ Score=\frac{T2\times\ Precision\ \times\ Recall}{Precision\ +\ Recall} \tag{6}\]  
where,  
* \(TP\) represents the number of attacks that are correctly classified as attacks
* \(FP\) represents the normal data classified as an attack
* \(TN\) represents the normal data classified as normal
* \(FN\) represents the attack as normal. The optimal parameters used by our 1D-CNN classifier are presented in Table 9. After detailed experimentation, the parameters presented in Table 9 were selected. From Table 8, it is evident that we achieved a training accuracy of 99.32%, a precision of 99.2%, a recall of 99.5% and an F1 score of 99.34% was obtained over 50 epochs. Testing accuracy of 98.96%, the precision of 98.7%, recall of 99.2%, and F1 score of 98.94% were obtained by the proposed 1D-CNN- based methodology. Furthermore, we also present a detailed comparison of our methodology with existing techniques; most of the researchers have used generic machine learning techniques along with a handful of researchers using ANN, deep learning, and CNN as their main architectures. A detailed comparison is presented in Table 10 which shows the architecture, dataset that is used and the accuracy obtained. Figure 2 presents the overall model performance over 50 epochs. We also report the loss of the model during training and validation in Figure 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Instances** & **Value** & \\ \hline Training & & 101,250 & \\ Testing & & 33,750 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Model Performance. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** & **Accuracy (\%)** \\ \hline Training & 99.2 & 99.5 & 99.34 & 99.32 \\ Validation & 98.7 & 99.2 & 98.94 & 98.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Dataset Distribution. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Technique** & **Dataset** & **Accuracy (\%)** \\ \hline
[26] & CNN & CICIDS 2017 & 96.55 \\
[33] & ANN & UNSW-NB15 & 84 \\
[34] & CNN, LSTM & CICIDS 2017 & 97.16 \\
[35] & LSTM, CNN, FNN & CICIDS 2017 & 98 \\
[36] & DNN & CICIDS 2017 & 96.5 \\
[37] & DCNN & NSLKDD & 85.22 \\
[38] & AE and DNN & CICIDS 2017, NSL-KDD & 98.92, 98.43 \\
[39] & DL, Feed-Forward back-Propagation Network & CICIDS 2017 & 98 \\
[40] & DNN-Multi Task Learning & CICIDS 2017, UNSW-NB15 & 87.50 \\
[41] & RNN BiLSTM & CIC IDS 2017 & 98.48 \\
[42] & LSTM & CICIDS 2017 & 96 \\
[43] & RNN and CNN & RedIRIS & 96.32 \\
[44] & CNN & NSL-KDD & 80.07 \\
[45] & CNN, Federated Learning & CICIDS 2017 & 98.8, 93 \\
[46] & NN & CICIDS 2017 & 77 \\ Proposed Method & 1D-CNN based DL system & CIC IDS 2017 & 98.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison with Existing Approaches.