Security, Trust and Privacy challenges in AI-driven 6G Networks

TL;DR

The paper addresses the security, trust, and privacy challenges that arise in AI-driven 6G networks, where a disaggregated, software-defined, and AI-enabled architecture expands the attack surface. It provides a taxonomy of AI-centric threats, distinguishing poisoning and evasion attacks on training and operation, and analyzes how trust models can be compromised through poisoning, model extraction, insider threats, and repudiation. It further surveys privacy risks driven by massive data generation and edge intelligence, and surveys building-block techniques such as Federated Learning, Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation, alongside mitigation strategies including zero-trust, robust authentication, and strong isolation. The work highlights the need for integrated, governance-aware defenses that account for AI explainability and cross-domain trust to enable secure, reliable deployment of 6G technologies into practice.

Abstract

The advent of 6G networks promises unprecedented advancements in wireless communication, offering wider bandwidth and lower latency compared to its predecessors. This article explores the evolving infrastructure of 6G networks, emphasizing the transition towards a more disaggregated structure and the integration of artificial intelligence (AI) technologies. Furthermore, it explores the security, trust and privacy challenges and attacks in 6G networks, particularly those related to the use of AI. It presents a classification of network attacks stemming from its AI-centric architecture and explores technologies designed to detect or mitigate these emerging threats. The paper concludes by examining the implications and risks linked to the utilization of AI in ensuring a robust network.