AI/ML for 5G and Beyond Cybersecurity

2025-05-23

AI Summary

Emerging AI/ML-native security threats—such as adversarial attacks, model extraction, and federated learning vulnerabilities—in 5G and future IoT systems lack unified threat definitions, differentiated attack modeling, and dynamic risk governance mechanisms. Method: This work formally defines the scope of AI/ML-native security threats, introduces a novel attack propagation analysis framework distinct from traditional systems, and proposes an interpretable, evolvable AI-driven security governance paradigm. It integrates anomaly detection, adversarial robust learning, federated learning, and real-time stream analytics to design an end-edge-cloud collaborative security monitoring and response architecture. Contribution/Results: The study delivers a technology roadmap for AI-empowered 5G security, identifies critical research gaps, and outlines standardization pathways—thereby supporting the development of highly trustworthy intelligent communication infrastructure. (149 words)

Technology Category

Philosophy and Ethics of AI: Privacy & SecurityMachine Learning: Learning on the Edge & Model CompressionComputer Vision: Adversarial Attacks & Robustness

Application Category

Security and Privacy: Security and privacy of machine learning and AI applicationsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The advancements in communication technology (5G and beyond) and global connectivity Internet of Things (IoT) also come with new security problems that will need to be addressed in the next few years. The threats and vulnerabilities introduced by AI/ML based 5G and beyond IoT systems need to be investigated to avoid the amplification of attack vectors on AI/ML. AI/ML techniques are playing a vital role in numerous applications of cybersecurity. Despite the ongoing success, there are significant challenges in ensuring the trustworthiness of AI/ML systems. However, further research is needed to define what is considered an AI/ML threat and how it differs from threats to traditional systems, as currently there is no common understanding of what constitutes an attack on AI/ML based systems, nor how it might be created, hosted and propagated [ETSI, 2020]. Therefore, there is a need for studying the AI/ML approach to ensure safe and secure development, deployment, and operation of AI/ML based 5G and beyond IoT systems. For 5G and beyond, it is essential to continuously monitor and analyze any changing environment in real-time to identify and reduce intentional and unintentional risks. In this study, we will review the role of the AI/ML technique for 5G and beyond security. Furthermore, we will provide our perspective for predicting and mitigating 5G and beyond security using AI/ML techniques.

Problem

• Investigating AI/ML threats in 5G and IoT security
• Ensuring trustworthiness of AI/ML-based cybersecurity systems
• Real-time monitoring for 5G security risks using AI/ML

Innovation

• AI/ML techniques for 5G cybersecurity
• Real-time monitoring of 5G threats
• Predicting and mitigating 5G security risks