# Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
## 6 Conclusions  
New tenants are welcomed in 5G networks but this participation can increase the threat level of a 5G core network. Using an adaptive security-based modular approach is beneficial to countering cyber threats in real-time. The KPIs defined in this paper help to deploy the vCTS module on PoPs in the context of 5G network protection. The suggested data filtering and validation approach allows us to undertake the logical separation of a network in the security region. Such security scanners help to share threat information between security NFs, strengthening the defense against expert hackers. Security regions  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Purposed** & **Architecture** & **DL Model** & **Technique** & \begin{tabular}{c} **Input** \\ **FVs Size** \\ \end{tabular} & **Batch Size** & **Classification** & \begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular} & **Dataset** & **Results** \\ \hline \begin{tabular}{c} Anomaly \\ Detection \\ Anomaly \\ Detection \\ \end{tabular} & Generative & DBN [29] & \begin{tabular}{c} Sliding \\ Window \\ \end{tabular} & 256 & \begin{tabular}{c} CPU \\ GPU \\ \end{tabular} & Binary & ReLU & CTU-13 & 99.34\% \\ \begin{tabular}{c} Anomaly \\ Detection \\ \end{tabular} & Generative & DBN [31] & \begin{tabular}{c} Sliding \\ Window \\ \end{tabular} & 256 & \begin{tabular}{c} CPU \\ GPU \\ \end{tabular} & Binary & ReLU & CTU-13 & 99.54\% \\ \begin{tabular}{c} Anomaly \\ Detection \\ \end{tabular} & Discriminative & CNN [32] & \begin{tabular}{c} Image \\ segmentation \\ \end{tabular} & 3 * & \begin{tabular}{c} Mini \\ Fixed \\ \end{tabular} & Binary & ReLU & CTU-13 & 72.57\% \\ \begin{tabular}{c} Anomaly \\ Detection \\ \end{tabular} & Discriminative & \
\begin{tabular}{c} CNN \\ \end{tabular} &
\begin{tabular}{c} Feature- \\ based \\ \end{tabular} & 24 & Fixed & Binary & Leaky ReLU & CTU-13 & 99.74\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis of past contributions and suggested model performance.  
enable operators to manage better the protection and swift detection of threats by initiating security VNFs on a slice or among slices. DL-based enhanced security NFs and VNFs with multitier approaches provide a comprehensive security view of shared slices and their virtual twins in a shared 5G environment and provide the stress-free integration of updated threat intelligence. Furthermore, comparing and contrasting the DL module to the limitation of past models in 5G network security enables us to provide the best solution in the current context. In the application context, improvements have been made to the input features dimension and computational complexity reduction with better response time and accuracy in outlier detection. The outcomes of all three proposed DL modules were recorded, with the best accuracy rate of 99.74% for botnet classification. For future work, we suggest researching the logical security management of a 5G network in both backhaul and fronthaul communication channels. The shared 5G slices and associated VNFs can be dealt with as \"virtual twins\" in a shared environment using service-level agreements with 5G tenants.  
Conceptualization, A.M. and A.W.; methodology, A.M., A.W. and S.K.; software, A.M., S.H. and A.K; validation, A.M., S.K. and A.M.A.; formal analysis, A.M., A.K., A.M.A., A.N. and S.H.; investigation, A.M.A., S.K., A.K. and A.N.; resources, A.W., A.N., A.M.A. and M.I.; data curation, A.M., A.K. and A.W.; writing--original draft preparation, A.M., A.M.A. and S.K.; writing--review and editing, A.M.A., S.K., A.W., A.K. and S.K.; visualization, A.M., A.W. and A.K.; supervision, A.N. and A.W.; project administration, A.N. and A.W.; funding acquisition, A.M.A. and M.I. All authors have read and agreed to the published version of the manuscript.  
Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding publication of this project.  
Our data is embedded in the paper, not separate.  
The authors declare no conflict of interest.