## 4 Results and Discussion
### Performance Evaluation
#### 4.2.2 Multi-Class Machine Learning Model
The \(S_{A}\) of R2L is improved from 84.13% to 91.32%, the \(S_{P}\) increased from 91.23% to 96.44%, the \(S_{A}\) of U2R increased from 28.49% to 43.83%, and the \(S_{P}\) increased from 66.88% to 75.02%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(s_{A}\) **(\%)** & \(s_{P}\) **(\%)** & \(s_{R}\) **(\%)** & \(S_{F1}\) **(\%)** \\ \hline kNN & 99.89 & 99.89 & 99.89 & 99.89 \\ \hline kNN with ADL & 99.23 & 99.49 & 98.74 & 99.15 \\ \hline Decision Tree & 99.82 & 99.82 & 99.82 & 99.82 \\ \hline DT with ADL & 99.58 & 99.72 & 99.58 & 99.65 \\ \hline Bayes & 94.84 & 95.91 & 94.84 & 95.06 \\ \hline Bayes with ADL & 98.77 & 95.25 & 99.75 & 97.70 \\ \hline BPNN & 98.84 & 98.86 & 98.84 & 98.05 \\ \hline BPNN with ADL & 99.15 & 99.36 & 99.82 & 99.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of existing classifiers with and without the proposed adaptive deep learning algorithm. Figure 7: Performance comparison of multi-classification using the existing four classification methods: KNN, Naive Bayes, BPNN and DT. To investigate the effect of ADL algorithm on the classifier performance at different \(\theta\) values, parametric analysis of \(\theta\) was carried out on selected datasets. The output efficiency of the model was determined by comparing the accuracy of the classifier at different values of \(\theta\). From the experimental results, \(\theta\) can affect the accuracy of the classifier by adjusting the dimensionality of feature extraction. When \(\theta\) was set to [0.1, 0.5], the \(S_{A}\) of the ADL algorithm improved gracefully. When \(\theta>0.6\), the \(S_{A}\) of the ADL algorithm is saturated and the stability of the ADL algorithm is high. Through experiements using the NSL-KDD dataset, the performance of the naive Bayesian algorithm classifier is greatly improved after processing by the ADL algorithm (see Table 6). The reason for the smaller performance improvement for the DoS data is that the main attacks in the network environment come from Dos attacks, so the number of Dos attacks is larger and better classification can be achieved without feature extraction. R2L and U2L are relatively small data in the dataset and the performance is greatly improved after feature extraction. The accuracy of R2L is improved from 84.13% to 91.32%, the accuracy rate increased from 91.23% to 96.44%, the accuracy rate of U2L increased from 28.49% to 43.83%, and the accuracy rate increased from 66.88% to 75.02%. Based on the CFS method, the data features of the subset are extracted using specific measurement indicators, the correlation matrices of different feature subsets are established, and the function values of the subset matrices are solved to select the best correlation feature matrix A subset. The results of CFS are shown in Table 7. Next, coding-based feature extraction methods often use ACE for feature extraction. Thus, for the sake of comparison, we also present results of ACE in Table 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & \(\mathbf{s_{A}}\) **(\%)** & \(\mathbf{s_{P}}\) **(\%)** & \(\mathbf{s_{R}}\) **(\%)** & \(\mathbf{S_{F1}}\) **(\%)** \\ \hline Normal & 99.91 & 98.65 & 99.90 & 99.78 \\ \hline DoS & 99.85 & 99.88 & 98.85 & 99.87 \\ \hline Probe & 98.83 & 99.75 & 99.87 & 99.34 \\ \hline R2L & 91.32 & 96.44 & 90.39 & 93.31 \\ \hline U2R & 42.83 & 75.02 & 42.85 & 54.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results using Naïve Bayes classifier together with the proposed ADL Algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & \(\mathbf{s_{A}}\) **(\%)** & \(\mathbf{s_{P}}\) **(\%)** & \(\mathbf{s_{R}}\) **(\%)** & \(\mathbf{S_{F1}}\) **(\%)** \\ \hline Normal & 99.58 & 100 & 97.2 & 83.9 \\ \hline DoS & 99.76 & 97.5 & 99.3 & 89.7 \\ \hline Probe & 99.81 & 84.7 & 99.7 & 91.6 \\ \hline R2L & 24.36 & 55.6 & 99.7 & 90.3 \\ \hline U2R & 60.17 & 82.3 & 99.7 & 72.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results using the Correlation Feature Selection Algorithm.