# Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
## 5 Discussion  
Table 6 shows a \"Comparative Analysis\" of the models' outcome in terms of the accuracy of suspicious data symptomizing. The data model domain can be described using multiple aspects, that is, the generative models explain how data are placed in the data space and analyzed in an unsupervised way using a probabilistic approach. The discriminative models are swift enough to detect outlier data, predict data, and define boundaries among data classes. The CTS module uses a deterministic approach to solve anomaly detection problems in a supervised way. The better results and behavior predicted by learning algorithms are based on feature engineering and model behavior. From a large number of features, the desirable and relevant attribute selection is able to reduce the data dimension to address the complexity of the problem. The learning of classification models can be improved with dimensional reduction techniques for feature selection [33]. Minimizing the feature size can improve performance, computational efficiency, generalization, and feature interpretability [34]. The redundant and strongly correlated variables cannot assure that the proof of additional information can increase training time [35]. Most of the features are irrelevant (either partially or completely) to the target output, and minimizing irrelevant features can reduce the learning process [36]. In the application context, improvements are made by minimizing the CTS input to 24 Fv for less computational complexity, better response time, and the improved robustness of the model. The unidirectional shallow architecture DBN model [23; 29; 31], as listed in the following Table, consumes 256 Fv as an input, causing redundant feature extraction with extra training time, the modification of weights, and biases at each learning cycle with more response time and the maximization of the computation. Furthermore, the large size input and the strong correlation of data features can affect output yield. Figure 13: Confusion matrix—CNN. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Class 0 & 1.00 & 0.97 & 0.98 & 19,709 \\ Class 1 & 0.97 & 1.00 & 0.98 & 19,926 \\ Accuracy & & & 0.98 & 39,635 \\ Macro Avg & 0.98 & 0.98 & 0.98 & 39,635 \\ Weighted Avg & 0.98 & 0.98 & 0.98 & 39,635 \\ \hline \end{tabular}
\end{table} 
Table 5: Classification score—CNN. The DBN is a greedy model that needs a large number of data features to perform. The ReLU activation function used in the DBN model faces the \"dying ReLU\" problem, which extends to the training process. To address this limitation, we substituted the ReLU activation with its improved form: the \"Leaky ReLU\" [37]. The Leaky ReLU can fix the \"dying ReLU\" concerns with a few slope and leak positive values at \"0\" to avoid the valuable information vanishing. Its training ability becomes faster when its mean activation reaches close to zero, which is able to increase training efficiency. The DBN follows the backpropagation technique through the modification of weight and fine-tuning in each iteration, as more depth in DBN can cause computational decay. Considering such an issue, for faster convergence, we adopted the Adam (adaptive moments) optimization technique to allow the model operators to learn adaptively with better speed and accuracy. The Adam optimization boosts the model convergence and the learning process, and it also helps to minimize the cost function or loss using operational parameters [38]. Moreover, the categorical cross-entropy loss function is used for encoded input labels (0, 1), ensuring better classification. On the other hand, the CNN [32] model uses the two-dimensional frame input data for image segmentation by transforming time-series data into multiple channels for model learning. The image input utilizes multi-dimensional data as a feature input, which can also decrease response time and increase the computation power of the model in real-time scenarios.