论文标题
当无线安全符合机器学习时：动机，挑战和研究方向
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
论文作者
论文摘要
由于无线介质的共享和广播性质，无线系统容易受到各种攻击，例如干扰和窃听。为了支持攻击和防御策略，机器学习（ML）提供了学习和适应无线通信特征的自动手段，而无线通信特征很难通过手工制作的功能和模型来捕获。本文讨论了启动ML和无线安全性的研究工作的动机，背景和范围。在ML的无线安全性，基于ML的攻击和防御解决方案以及无线领域中新兴的对抗ML技术的研究方向上，确定了无线攻击解决方案，并确定了培养ML和无线安全性的研究工作的路线图。
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense strategies, machine learning (ML) provides automated means to learn from and adapt to wireless communication characteristics that are hard to capture by hand-crafted features and models. This article discusses motivation, background, and scope of research efforts that bridge ML and wireless security. Motivated by research directions surveyed in the context of ML for wireless security, ML-based attack and defense solutions and emerging adversarial ML techniques in the wireless domain are identified along with a roadmap to foster research efforts in bridging ML and wireless security.
