# ML-Based 5G Network Slicing Security:
## 2 Comparison with Existing Survey Articles and Roadmap
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Authors** & **Main Contribution** & **SDN and NPV Based NS** & **M. Based NS** & **Threats and Attacks** & **Sevoiry Solutions** & **Research Challenges** \\ \hline
[11] & Author contributed a comprehensive survey with updated solutions related to NS using SDN and NPV. & ✓ & - & ✓ & ✓ & ML based NS is not covered \\ \hline
[12] & This survey covers challenges for network slicing domains such as across, transport and control. & ✓ & - & - & ✓ & ML based NS and security considerations are not covered \\ \hline
[13] & The author contributed the importance of SDN and NPV, to overcome the traditional problems in network. & ✓ & - & - & ✓ & ML based NS and security considerations are not covered \\ \hline \multicolumn{6}{p{142.3 pt}}{The author discussed the key technologies} \\ \multicolumn{6}{p{142.3 pt}}{The author discussed the key technologies} \\ \multicolumn{6}{p{142.3 pt}}{such as NPV, modulation, dynamic service} \\ \multicolumn{6}{p{142.3 pt}}{containing an not covered} \\ \multicolumn{6}{p{142.3 pt}}{containing an not covered} \\ \multicolumn{6}{p{142.3 pt}}{containing an not covered} \\ \multicolumn{6}{p{142.3 pt}}{containing a network} \\ \multicolumn{6}{p{142.3 pt}}{containing a network} \\ \multicolumn{6}{p{142.3 pt}}{This survey provided a study on field Load} \\ \multicolumn{6}{p{142.3 pt}}{consideration of the network} \\ \multicolumn{6}{p{142.3 pt}}{consideration of the network} \\ \multicolumn{6}{p{142.3 pt}}{consideration of the network} \\ \multicolumn{6}{p{142.3 pt}}{consideration of the network} \\ \multicolumn{6}{p{142.3 pt}}{consideration of SDN and NPV.} \\ \multicolumn{6}{p{142. Some surveys focus on the architecture and challenges in the NS. [PERSON] et al. [16] emphasized 5G architecture along with the capabilities of SDN, NFV, and the application of 5G. [PERSON] et al. [17] discuss the challenges of NS and its architecture. [PERSON] et al. [18] and [PERSON] et al. [19] focused on resource allocation in slices. [PERSON] et al. [20] and [PERSON] et al. [23] discussed the advancement of the NS in IoT and smart applications, as well as challenges in NS due to the evolution of IoT. [PERSON] et al. [21] give solutions to the challenges in IoT service management. [PERSON] et al. [22] explained how slicing can be achieved in RAN and CN networks along with the enabling technologies and solutions. These surveys emphasized NS and provided solutions to the issues but lacked machine learning-based Network Slicing. Some studies recommended ML approaches for future research to counter these challenges. Our survey mainly emphasizes Machine Learning-based Network Slicing and new blend-up solutions based on ML, how ML can be applied over various stages in the NS life cycle, and network components and 5G NS application parameters. Figure 2 shows the evolution of Network Slicing, the year of conceptualization [24], implementation [25; 26] network virtualization [27; 28] the concept of SDN [29] and NFV [30; 31], and different services for the users [32; 33; 34; 35; 36]. From the year 2015, research in the field of Network Slicing accelerated, with the result of increasing the quality of services. Various Machine Learning (ML) [37; 38; 39] and Deep Learning (DL) [40; 41; 42; 43; 44] algorithms are being applied to manage slices, user requests, slice admissions, resources, and traffic in 5G network slicing. Various optimization algorithms [45; 46] are also utilized to optimize the network functions.