The division of one physical 5G communications infrastructure into several virtual network slices with distinct characteristics such as bandwidth, latency, reliability, security, and service quality is known as 5G network slicing. Each slice is a separate logical network that meets the requirements of specific services or use cases, such as virtual reality, gaming, autonomous vehicles, or industrial automation. The network slice can be adjusted dynamically to meet the changing demands of the service, resulting in a more cost-effective and efficient approach to delivering diverse services and applications over a shared infrastructure. This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.

翻译：将一个物理5G通信基础设施划分为多个具有不同特性（如带宽、时延、可靠性、安全性和服务质量）的虚拟网络切片，称为5G网络切片。每个切片都是一个独立的逻辑网络，满足特定服务或应用场景的需求，例如虚拟现实、游戏、自动驾驶车辆或工业自动化。该网络切片可根据服务需求的变化动态调整，从而在共享基础设施上提供多样化服务和应用时实现更具成本效益和高效的方案。本文评估了多种机器学习技术，包括逻辑回归模型、线性判别模型、k近邻模型、决策树模型、随机森林模型、SVC BernoulliNB模型和GaussianNB模型，以研究每种模型在网络切片检测中的准确率和精确度。报告还对5G网络切片进行了概述。