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 largescale 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 rapid evolution of autonomous vehicular technologies demands advanced solutions for reliable navigation in diverse and often unfamiliar environments. This paper introduces a novel CNN-based Federated Learning approach for vehicular systems, designed to enhance their self-driving adaptability and decision-making capabilities in novel environments through collaborative training. Our method leverages the decentralized nature of FL to enable vehicles to learn collectively from shared experiences while maintaining the privacy of individual data. We utilize the CARLA simulation environment to generate a wide range of driving scenarios, including multiple vehicles operating under varied weather conditions. This diverse dataset serves as a testbed for our FL framework, allowing us to evaluate its effectiveness in accurately predicting steering wheel angles while simultaneously adapting to different environmental challenges. Our results demonstrate that vehicles trained using our FL framework exhibit enhanced performance in predictive analytics, showing greater resilience to environmental changes and improved decision-making in real-time scenarios with a much lower computational complexity.

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.