通过对抗镜头检查5G及更高级别的机器学习
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
摘要：
在深度学习的最新进展的推动下，利用隐藏在大量数据中的丰富信息并解决难以建模/解决的问题（例如，资源分配问题），目前在移动网络领域，围绕5G及以后基于数据驱动的AI/ML的网络自动化、控制和分析的变革潜力，人们感到非常兴奋。在这篇文章中，我们通过强调跨越多种类型的ML（监督/无监督/RL）的对抗维度，并通过三个案例研究来支持这一点，提出了在5G背景下使用AI/ML的谨慎观点。我们还讨论了减轻这种对抗性ML风险的方法，为评估ML模型的鲁棒性提供了指导方针，并呼吁人们更普遍地关注5G中面向ML的研究问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
