## 2 Materials and Methods
### Data Preprocessing

Table 3: UNSW-NB15 after data processing. Maximum Relevance: The mRMR algorithm computes the level of correlation between each feature and the target variable. It assigns higher priority to features with stronger correlations with the target variable. \[\text{maxD}(\text{S},\text{c}),\text{D}=\frac{1}{|S|}\sum\
olimits_{f_{i}\in S}I (f_{i},c) \tag{3}\]  
Minimum Redundancy: The mRMR algorithm evaluates the level of redundancy among features. It emphasizes selecting features that have minimal redundancy with the already selected features. This ensures that the selected feature subset exhibits a low correlation among its features and avoids including redundant information. \[\text{minR}(\text{S}),\text{R}=\frac{1}{|S|^{2}}\sum\
olimits_{f_{i}\in S}I (f_{i},f_{j}) \tag{4}\]  
The final mRMR value is computed by considering both maximum relevance and minimum redundancy criteria. Features are ranked in descending order of their scores, and features with high scores are progressively added to the feature subset. This approach ensures that features with high relevance to the target variable and minimal redundancy with the existing feature set are included in the subset. \[maxJ(D,R),J=D-R \tag{5}\]  
where S is the feature set; \(f_{i}\) is the feature; c is the target category; \(I(f_{i},c)\) is the mutual information between the feature and the target category C; \(I(f_{i},f_{j})\) is divided into the information between the feature and the feature. The mRMR value combines the correlation between features and the target variable with the degree of redundancy between features. Its purpose is to select a subset of features that are both highly informative and unique. The coefficient ranges from 0 to 1, where a higher absolute value indicates a stronger correlation between the two variables. The strength of the correlation is typically determined based on the following Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Features Extracted from Each Threshold mRMR Feature Column**} \\
**Dataset** & **0.0–0.2** & **0.2–0.4** & **0.4–0.6** & **0.6–0.8** & **0.8–1.0** \\ \hline \multirow{2}{*}{NSL-KDD} & 98, 40, 7, 57, 112, 42, & 65, 35, 34, 36, 22, & 21, 116, 33, 29, 8, & 37, 27, 26, 39, 38, 5, & 2, 3, 31, 30, 20, 4, \\  & 86, 83, 111, 19... & 108, 88, 41, 23 & 10, 28, 14, 13 & 25, 6, 1, 32 & 24, 9 \\ \hline \multirow{2}{*}{UNSW-NB15} & 39, 46, 118, 35, 34, & 15, 23, 2, 16, 12, 11, & 29, 8, 10, 28, 33, 14, & \multirow{2}{*}{5, 25, 6, 1, 32, 38} & \multirow{2}{*}{9, 24, 9} \\  & 137, 48, 130, 154... & 18, 19, 17, 20, 27, 36, & 7, 31, 3, 30, 13, 22, & \multirow{2}{*}{5, 25, 6, 1, 32, 38} & \multirow{2}{*}{9, 24, 9} \\  & 26 & 21, 37 & & \\ \hline \multirow{2}{*}{CICIDS2017} & 56, 60, 31, 32, 58, 57, & 27, 24, 77, 74, 69, 76, & 21, 16, 35, 37, 15, & 18, 6, 67, 39, 10, 4, & \multirow{2}{*}{42, 40, 41, 52} \\  & 44, 30, 71, 48, 43... & 3, 28, 22, 9, 17, 13 & 23, 1, 14 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristic columns for each threshold interval. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Range of mRMR Thresholds**} & **Correlation Strength** \\ \hline
0.0–0.2 & Very weak or no correlation \\
0.2–0.4 & Weak correlation \\
0.4–0.6 & Moderate correlation \\
0.6–0.8 & Strongly related \\
0.8–1.0 & Extremely strong correlation \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation Strength Correspondence Table.