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

by   Muhammad Usama, et al.

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.


DataPerf: Benchmarks for Data-Centric AI Development

Machine learning (ML) research has generally focused on models, while th...

Machine learning in acoustics: a review

Acoustic data provide scientific and engineering insights in fields rang...

Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Adversarial Machine Learning (AML) is emerging as a major field aimed at...

Automated machine learning: AI-driven decision making in business analytics

The realization that AI-driven decision-making is indispensable in today...

DBNet: Leveraging DBMS for Network Automation

We present DBNet, a data-driven network automation framework built on to...

Please sign up or login with your details

Forgot password? Click here to reset