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Modern 5G systems are not standalone systems that come from a single vendor or supplier. In fact, it comprises an integration of complex software, hardware, and cloud services that are developed by specialist entities. Moreover, these components have a supply chain that may have linkages and relationships between different vendors. A mobile network operator relies on the functionality and integrity of all the constituent components and their suppliers to ensure the communication network’s confidentiality, integrity, and availability. While the operator can employ cybersecurity best practices itself, it does not have control over the cybersecurity practices of its immediate vendors and the wider supply chain. Recently, attackers have exploited cyber vulnerabilities in the supplier network to launch large-scale breaches and attacks. Hence, the supply chain becomes a weak link in the overall cybersecurity of the 5G system. Hence, it is becoming crucial for operators to understand the cyber risk to their infrastructure, with a particular emphasis on the supply chain risk. In this paper, we systematically break down and analyze the 5G network architecture and its complex supply chains. We present an overview of the key challenges in the cybersecurity of 5G supply chains and propose a systemic cyber risk assessment methodology to help illuminate the risk sources and use it to manage and mitigate the risk. It will guide stakeholders in establishing a secure and resilient 5G network ecosystem, safeguarding the backbone of modern digital infrastructure against potential cybersecurity threats. 

The deployment of fifth-generation (5G) networks across various industry verticals is poised to transform communication and data exchange, promising unparalleled speed and capacity. However, the security concerns related to the widespread adoption of 5G, particularly in mission-critical sectors, present significant challenges. This article investigates the potential of a Zero Trust (ZT) security philosophy as a viable countermeasure to these concerns. It delves into the practicalities of implementing ZT principles within 5G networks, with a specific focus on harnessing AI/ML technologies for proactive security measures, dynamic policy adaptations, and advanced risk assessments. Further, the article underscores the importance of developing a tailored ZT maturity model for 5G networks. Furthermore, the paper outlines key future research directions aimed at improving the ZT maturity of 5G deployments, contributing to the safe and secure integration of 5G technology in various sectors. 

Effective resource orchestration for network slicing is critical for optimizing the performance of diverse applications running on next generation communication networks. This paper presents a novel approach that leverages advancements in multi-agent reinforcement learning (MARL) to adaptively learn the resource requirements of various applications in network slices and orchestrate resources in real-time. Our proposed MARL-based orchestration scheme aims to balance the varying requirements of individual network slices, ensuring optimal performance amid dynamic application deployments with limited network information. Simulation results and comparative analyses validate the efficiency and efficacy of our methodology, demonstrating its superiority over traditional methods in terms of system performance and resource utilization. Simulation results indicate that our strategy significantly enhances system utility and efficiency, particularly with limited resources. 

Modern fifth-generation (5G) networks are increasingly moving towards architectures characterized by softwarization and virtualization. This paper addresses the complexities and challenges in deploying applications and services in the emerging multi-tiered 5G network architecture, particularly in the context of microservices-based applications. These applications, characterized by their structure as directed graphs of interdependent functions, are sensitive to the deployment tiers and resource allocation strategies, which can result in performance degradation and susceptibility to failures. Additionally, the threat of deploying potentially malicious applications exacerbates resource allocation inefficiencies. To address these issues, we propose a novel optimization framework that incorporates a probabilistic approach for assessing the risk of malicious applications, leading to a more resilient resource allocation strategy. Our framework dynamically optimizes both computational and networking resources across various tiers, aiming to enhance key performance metrics such as latency, accuracy, and resource utilization. Through detailed simulations, we demonstrate that our framework not only satisfies strict performance requirements but also surpasses existing methods in efficiency and security.