ML-Based 5G Network Slicing Security: A Comprehensive Survey

TLDR

Fifth‑generation networks enable mobile broadband, massive machine‑type communication, and ultra‑reliable low‑latency services by partitioning the physical network into virtual slices, but this slicing introduces security challenges that adversaries exploit. The paper investigates security challenges throughout the 5G network‑slice lifecycle and compares existing surveys while mapping machine‑learning solutions to slice functions. It applies machine‑learning and deep‑learning techniques across all slice‑management stages—planning, design, deployment, monitoring, fault detection, and security—to mitigate attacks and map solutions to slice functions.

Abstract

Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.