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

TL;DR

This paper discusses the security, trust, and privacy challenges in AI-driven 6G networks, focusing on attack classification and mitigation strategies within the evolving AI-centric infrastructure.

Contribution

It provides a comprehensive classification of AI-related attacks in 6G networks and explores technologies for detecting and mitigating these emerging threats.

Findings

Classification of network attacks in AI-driven 6G

Analysis of AI-related security threats and vulnerabilities

Discussion of mitigation technologies for 6G security

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.