A survey on 5G private and B5G network threats and safeguarding AI-based security mechanisms through the layered analysis

Abstract

The fifth-generation (5G) mobile network has shifted the paradigm in connectivity, high-speed data transmission, ultra-low latency, ultra-high throughput, multisense transmission, and ultra-high reliability. Today, industries are increasingly adopting 5G private networks, which also handle sensitive data related to business information, trade secrets, and personal data. Attacks on 5G private networks can potentially result in losing a competitive advantage since different security threats and vulnerabilities progressively target these networks. The unique infrastructure of the 5G network architecture and key enabling technologies exposes them to various vulnerabilities that attackers can target to breach sensitive data, steal information, and disrupt critical systems. Therefore, paying special attention to the security issues of 5G private networks is essential. The advancement of future wireless technology came about because of the different and diverse nature of connected devices, in contrast to the previous generation of mobile networks. The B5G network provides support to open network platforms, open interfaces, and the integration of different key enabling technologies helps manage network services and deploy new services needed for diverse requirements. At the same time, it has increased the attack surface compared to previous-generation networks. Therefore, it is imperative to conduct a review that focuses on addressing and classifying the different emerging threats in the private 5G and B5G networks in a distinctive way. In this paper, we have adopted a layered architecture from an industrial use case of 5G private networks to identify and classify different threats in 5G private networks. The study also characterized and modeled the different threats using information on the type of attack, entry points, and impact of the attack on the architecture layer. Moreover, an analysis of key enablers in 5G private and B5G networks and information on security threats and cyber-attacks is also presented. To accommodate the emerging threats in next-generation wireless technology, we have classified and modeled the different threats in the B5G domain using the common involved layer, which includes perception, network, and application layers in Hexa-x E2E and 6G IoT-enabled architecture. Organizations and projects that do not actively engage in cutting-edge technologies will lose their competitive advantage in the evolving technological landscape. This study has mapped the identified threat categories with different types of threats that could occur at different layers and assessed the entry points of the attacker. We have also identified the attack’s impact at each layer using security requirements related to confidentiality, availability, and integrity. Furthermore, this study reviews different AI-enabled solutions that can add value in preserving the security of 5G and B5G networks. Through this review analysis, we can conclude that the layered approach is quite beneficial in identifying the different threats and what security solutions can be offered to mitigate them.