# ML-Based 5G Network Slicing Security:
## 4 Machine Learning Based Network Slicing  
Machine Learning (ML) is a branch of Artificial Intelligence (AI) based on the ideology of self-learning, where the machine or the system can self-learn based on the previous results or the dataset gathered with less human intervention. It has the potential to provide more straightforward solutions to complex problems by learning from and analyzing bulk data. It analyses and adapts the changes as per the environment and predicts the near future with accurate results. With the growing amount of data and market competition, ML technology is being adopted by most industries and sectors such as the government, healthcare, retail, financial services, and transportation. By extracting insight from the data, the organizations can work more effectively and procure advantages over the competitors. 5G networks are becoming more complex due to the emergence of an exceptionally massive number of newly connected devices and different types of services that can be termed as data. With the increase in the volume of traffic and service requests for diverse users and applications, there has been an increase in the need for creating network slices over physical networks to optimize the services and the requests by maintaining the robustness, workload, and security in the network with the involvement of ML technologies. ML can be considered an automation in various network operations and management such as planning and design, construction and deployment, on-demand adaptive network configuration, monitoring, fault detection, and security. Figure 8 portrays various network functions in network slicing and their relevant ML techniques. ML also provides algorithms to retrieve information from raw data, perceptive advice, and prediction based on learning [39]. Focusing on machine learning-based network slicing, numerous research has been published to emphasize the use of ML and DL models along with the network components, application parameters, and security considerations, as shown in Table 4.  
Figure 7: ETSI architecture. \begin{table}
\begin{tabular}{l l l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multirow{2}{*}{**Key Coordination**} & \multirow{2}{*}{**M. Applied**} & \multicolumn{6}{c}{**Network Analysis/Component**} & \multicolumn{6}{c}{**50 Cs Network Application Parameters**} & \multirow{2}{*}{**Scentry Consideration**} \\ \cline{3-14}  & & & **Z1** & **Z2** & **Z3** & **Z4** & **Z5** & **Z5** & **Z5** & **Z5** & **Z5** &  
In case of slice admission in the 5G network, infrastructure providers (InPs) have to effectively and efficiently serve a variety of services, improve resource efficiency, and reduce the cost based on the service request, which can be scaled up or scaled down as per the requirements and service time. In such cases, the profit of the InPs can only be increased when they accept as many slice requests as possible and check the variations in the slice's requirements to avoid performance degradation. Here, it is necessary to create an intelligent slice admission scheme for systematic admission; the ML approach can be best suited to preventing the obstruction in the infrastructure and obtaining the maximum profit for an InP. [PERSON] et al. [60] have modeled an ML approach with two algorithms working together, i.e., supervised learning and reinforcement learning. Supervised learning-based big data analytics collected historical data and observed the change in the requirements of the incoming requests and the present slices. In contrast, a reinforcement learning-based solution checks the circumstances where obstruction can occur in the system or infrastructure. From these observations and learning, they only admitted those slices that may not give rise to any obstruction and may not lead to degradation. Consequently, the profit of InPs will rise.