论文标题
工业控制系统中入侵检测的机器学习：应用，挑战和建议
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations
论文作者
论文摘要
机器学习的方法正在设计用于设计对网络攻击的工业控制系统。这样的方法着重于两个主要领域：使用通过网络数据包获得的信息检测网络级别的入侵，以及使用代表系统物理行为的数据在物理过程级别检测异常。该调查重点介绍了用于入侵和异常检测的机器学习的四种方法，即，监督，半监督，无监督和增强学习。仔细选择，分析并将其放置在7维空间中，以便于比较。该调查针对的是研究人员，学生和从业人员。确定了使用方法和研究差距相关的挑战，并提出了填补空白的建议。
Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the detection of intrusions at the network-level using the information acquired through network packets, and detection of anomalies at the physical process level using data that represents the physical behavior of the system. This survey focuses on four types of methods from machine learning in use for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. Literature available in the public domain was carefully selected, analyzed, and placed in a 7-dimensional space for ease of comparison. The survey is targeted at researchers, students, and practitioners. Challenges associated in using the methods and research gaps are identified and recommendations are made to fill the gaps.
