Globally, the external internet is increasingly being connected to industrial control systems. As a result, there is an immediate need to protect these networks from a variety of threats. The key infrastructure of industrial activity can be protected from harm using an intrusion detection system (IDS), a preventive mechanism that seeks to recognize new kinds of dangerous threats and hostile activities. This review examines the most recent artificial-intelligence techniques that are used to create IDSs in many kinds of industrial control networks, with a particular emphasis on IDS-based deep transfer learning (DTL). DTL can be seen as a type of information-fusion approach that merges and/or adapts knowledge from multiple domains to enhance the performance of a target task, particularly when labeled data in the target domain is scarce. Publications issued after 2015 were considered. These selected publications were divided into three categories: DTL-only and IDS-only works are examined in the introduction and background section, and DTL-based IDS papers are considered in the core section of this review. By reading this review paper, researchers will be able to gain a better grasp of the current state of DTL approaches used in IDSs in many different types of network. Other useful information, such as the datasets used, the type of DTL employed, the pre-trained network, IDS techniques, the evaluation metrics including accuracy/F-score and false-alarm rate, and the improvements gained, are also covered. The algorithms and methods used in several studies are presented, and the principles of DTL-based IDS subcategories are presented to the reader and illustrated deeply and clearly

翻译：全球范围内，外部互联网正越来越多地与工业控制系统相连接。因此，亟需保护这些网络免受各种威胁。入侵检测系统（IDS）作为一种旨在识别新型危险威胁和敌对活动的预防机制，可保护工业活动的关键基础设施免受损害。本综述考察了用于构建各类工业控制网络IDS的最新技术，特别聚焦于基于深度迁移学习（DTL）的IDS。DTL可视为一种信息融合方法，它合并和/或调整来自多个领域的知识，以提升目标任务的表现，尤其在目标领域标注数据稀缺时效果显著。本研究考虑了2015年之后发表的文献，所选文献被分为三类：引言与背景部分探讨了纯DTL和纯IDS研究，而综述核心部分则聚焦于基于DTL的IDS论文。通过阅读本综述，研究人员将能更深入地理解DTL方法在各类网络IDS中的应用现状。此外，本文还涵盖了其他有用信息，如所用数据集、DTL类型、预训练网络、IDS技术、评估指标（包括准确率/F值及误报率），以及所取得的性能提升。文中展示了多项研究中采用的算法与方法，并向读者深入清晰地阐释了基于DTL的IDS子类别的原理。