Examining Machine Learning for 5G and Beyond through an Adversarial Lens

TL;DR

This paper critically examines the vulnerabilities of AI/ML techniques in 5G networks, emphasizing adversarial risks and proposing mitigation strategies to ensure robust deployment.

Contribution

It provides a comprehensive adversarial perspective on ML in 5G, including case studies, evaluation guidelines, and discussion on research challenges.

Findings

Adversarial attacks pose significant risks to ML-based 5G systems.

Case studies demonstrate vulnerabilities across supervised, unsupervised, and reinforcement learning.

Guidelines for evaluating ML robustness in 5G are proposed.

Abstract

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/RL) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.