Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS
Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS
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DOI：
10.1109/comst.2020.3036778
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发表时间：
2021-01-01
影响因子：
35.6
通讯作者：
Liu, Chunmei
中科院分区：
文献类型：
--
作者：
Olowononi, Felix O.;Rawat, Danda B.;Liu, Chunmei
网络物理系统（CPS）的特点是它们能够整合物理和信息或网络世界。它们在关键基础设施中的部署显示了改变世界的潜力。然而，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。CPS对网络问题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，CPS安全性已经从使用密码学和其他访问控制技术防止入侵者获得对系统的访问的角度进行了研究。因此，大多数研究工作都集中在CPS中的攻击检测上。然而，在一个对手不断增加的世界里，完全防止CPS受到对抗性攻击变得越来越困难，因此需要专注于使CPS具有弹性。弹性CPS旨在承受中断并在对手的操作下保持功能。构建弹性CPS的主要方法之一依赖于机器学习（ML）算法。然而，从最近对对抗性ML的研究中，我们认为用于保护CPS的ML算法本身必须具有弹性。因此，本文的目的是全面调查弹性CPS使用ML和弹性ML应用于CPS时之间的相互作用。文章最后总结了一些研究趋势和未来的研究方向。此外，通过本文，读者可以深入了解基于ML的安全性和针对CPS的安全ML的最新进展和对策，以及这一活跃研究领域的研究趋势。
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.