Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

中文翻译：

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网络网络物理系统的弹性机器学习：机器学习安全性调查，以确保CPS机器学习的安全

网络物理系统（CPS）的特点是具有整合物理和信息或网络世界的能力。它们在关键基础设施中的部署已显示出改变世界的潜力。但是，利用这种潜力受到其关键性质以及网络攻击对人类，基础设施和环境的深远影响的限制。在CPS中引起网络关注的吸引力来自通过无线通信介质将信息从传感器发送到执行器的过程，从而扩大了攻击面。传统上，从防止入侵者使用密码术和其他访问控制技术来访问系统的角度研究了CPS安全。因此，大多数研究工作都集中在CPS中攻击的检测上。然而，在当今对手不断增加的世界中，完全阻止CPS遭受对抗攻击变得越来越困难，因此需要集中精力使CPS具有复原力。弹性CPS旨在抵抗干扰并在敌人操作的情况下仍能正常运行。探索用于构建弹性CPS的主要方法之一取决于机器学习（ML）算法。但是，从对抗性ML的最新研究兴起之后，我们认为用于保护CPS的ML算法本身必须具有弹性。因此，本文旨在全面研究使用ML的弹性CPS和应用于CPS的弹性ML之间的相互作用。本文总结了许多研究趋势和有希望的未来研究方向。此外，在本文中，