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

Muhammad Usama, Rupendra Nath Mitra, Inaam Ilahi, Junaid Qadir, Mahesh K. Marina

Research output: Contribution to journalSpecial issuepeer-review

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 multipletypes 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.
Original languageEnglish
Number of pages7
JournalIEEE Internet Computing
Early online date5 Jan 2021
DOIs
Publication statusE-pub ahead of print - 5 Jan 2021

Keywords

  • 5G and Beyond Mobile Networks
  • Adversarial Machine Learning
  • Security

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