Globally, the external Internet is increasingly being connected to the contemporary industrial control system. As a result, there is an immediate need to protect the network from several threats. The key infrastructure of industrial activity may be protected from harm by using an intrusion detection system (IDS), a preventive measure mechanism, to recognize new kinds of dangerous threats and hostile activities. The most recent artificial intelligence (AI) techniques used to create IDS in many kinds of industrial control networks are examined in this study, with a particular emphasis on IDS-based deep transfer learning (DTL). This latter can be seen as a type of information fusion that merge, and/or adapt knowledge from multiple domains to enhance the performance of the target task, particularly when the labeled data in the target domain is scarce. Publications issued after 2015 were taken into account. These selected publications were divided into three categories: DTL-only and IDS-only are involved in the introduction and background, and DTL-based IDS papers are involved in the core papers of this review. Researchers will be able to have a better grasp of the current state of DTL approaches used in IDS in many different types of networks by reading this review paper. Other useful information, such as the datasets used, the sort of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false alarm rate (FAR), and the improvement gained, were also covered. The algorithms, and methods used in several studies, or illustrate deeply and clearly the principle in any DTL-based IDS subcategory are presented to the reader.

翻译：全球范围内，外部互联网越来越与当代工业控制系统相连。因此，急需保护网络免受多种威胁。入侵检测系统（IDS）作为一种预防措施机制，可以识别新类型的危险威胁和敌对活动，从而保护工业活动的关键基础免受损害。本文研究了最新的人工智能（AI）技术在许多种工业控制网络中创建IDS的应用，特别是以基于深度转移学习（DTL）的IDS为重点。DTL可以被看作是一种信息融合，将和/或调整来自多个领域的知识，以增强目标任务的性能，特别是当目标领域中标记数据很少时。本文考虑了2015年以后发表的论文。这些选定的出版物分为三个类别：介绍和背景涉及DTL或IDS，而基于DTL的IDS论文则涉及本综述的核心论文。通过阅读本综述，研究人员将能够更好地了解DTL在许多不同类型网络中用于IDS的现状。其他有用的信息，例如使用的数据集、所采用的DTL类型、预训练网络、IDS技术、包括准确度/ F-score和假报警率（FAR）在内的评估指标以及所获得的改进，也都有所涉及。还向读者介绍了若干研究中所使用的算法和方法，或深入而清晰地说明了基于DTL的IDS子类别的原理。