# Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
## 5 Experimental Methodology
### Phase-I Experiment  
In the first phase of the experiments, a DL model is adopted for intrusion detection. The architecture of this model comprises four hidden layers with a neuron configuration of 200, 100, 50, and 20 neurons, respectively, for the input and output layers. Activation functions--ReLu for the hidden layers and Softmax for the output layer--are applied to classify normal and malware labels. The model's parameters are detailed in Table 3 below. Once the model's hyperparameters are fine-tuned and the dataset is preprocessed,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Number** \\ \hline Normal & 14,040 \\ \hline Malware & 99,219 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of normal (benign) and malware (threat) data in the CIC2019 network dataset.  
the model training commences using features derived through Pearson correlation-based feature deduction method as depicted in Figure 2. In Figure 2, the correlation strength of the linear relationship between features is depicted. The correlation coefficient value ranges from \(1\) to \(-1\). A value of \(1\) signifies a strong positive linear correlation, depicted by a dark color in the figure. Conversely, a value of \(-1\) indicates a strong negative linear correlation, portrayed in a light color. Values close to \(1\), typically above \(0.60\), are considered a strong correlation, suggesting a significant relationship between the features. Conversely, correlation coefficients below this threshold are regarded as weak correlations and are often disregarded in the analysis. The extracted features utilized in our analysis are outlined in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Correlations** \\ \hline Source Port & 0.885809 \\ \hline Protocol & 0.805606 \\ \hline Total Length of Fwd Packets & 0.797673 \\ \hline Fwd Packet Length Max & 0.952294 \\ \hline Fwd Packet Length Min & 0.992838 \\ \hline Fwd Packet Length Mean & 0.988572 \\ \hline Flow Bytes/s & 0.643855 \\ \hline Flow Packets/s & 0.557369 \\ \hline Fwd Packets/s & 0.558265 \\ \hline Min Packet Length & 0.994147 \\ \hline Max Packet Length & 0.825010 \\ \hline Packet Length Mean & 0.988031 \\ \hline URG Flag Count & 0.679175 \\ \hline Down/Up Ratio & 0.691483 \\ \hline Average Packet Size & 0.990900 \\ \hline Avg Fwd Segment Size & 0.988572 \\ \hline Subflow Fwd Bytes & 0.797673 \\ \hline Init\_Win\_bytes\_forward & 0.503775 \\ \hline Inbound & 0.940320 \\ \hline \hline \end{tabular}
\end{table}
Table 4: High correlation features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning rate & 0.001 \\ \hline Batch size & 512 \\ \hline Epochs & 20 \\ \hline Loss function & Binary cross-entropy \\ \hline Dropout & 0.4 \\ \hline Optimizer & Adam \\ \hline Activation function -hidden layers & ReLU \\ \hline Activation function -output layer & Sigmoid \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of DNN model.  
The model initiates training using these non-crafted (clean) data features. Subsequently, the testing phase follows the training, focusing on a binary classification problem (zero for normal labels and one for malware labels). Multiple evaluation metrics detailed in Section 4.3 are employed to assess the model's efficiency and performance. The experiment's results are analyzed and compared to the subsequent section's outcomes.