Examining Machine Learning for 5G and Beyond through an Adversarial Lens
| Autor: | Inaam Ilahi, Rupendra Nath Mitra, Muhammad Usama, Mahesh K. Marina, Junaid Qadir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Networks and Communications Computer science Context (language use) 02 engineering and technology Adversarial machine learning Machine learning computer.software_genre Machine Learning (cs.LG) Computer Science - Networking and Internet Architecture 0202 electrical engineering electronic engineering information engineering Reinforcement learning Networking and Internet Architecture (cs.NI) Context model business.industry Deep learning 020206 networking & telecommunications Adversarial Machine Learning Transformative learning Analytics Security Resource allocation Artificial intelligence business computer 5G and Beyond Mobile Networks |
| Zdroj: | Usama, M, Mitra, R N, Ilahi, I, Qadir, J & Marina, M K 2021, ' Examining Machine Learning for 5G and Beyond through an Adversarial Lens ', IEEE Internet Computing, vol. 25, no. 2, pp. 26-34 . https://doi.org/10.1109/MIC.2021.3049190 |
| DOI: | 10.1109/MIC.2021.3049190 |
| Popis: | 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 artificial intelligence/machine learning (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/reinforcement learning) 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. |
| Databáze: | OpenAIRE |
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