This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human–machine behaviour monitoring requirements.

这份调查报告讨论了在制造业中使用人工智能（AI）技术的机会和威胁，同时考虑了这种技术的进攻性和防御性使用。首先介绍了Industry 4.0概念，并在这种情况下了解了AI的使用。然后提供了安全原理和检测技术的要素，这些要素适用于构成制造系统主要攻击面的运营技术（OT）。由于某些入侵检测系统（IDS）已经包含一些基于AI的技术，因此我们将重点放在用于入侵检测的现有基于机器学习和数据挖掘的技术上。本文介绍了所使用的主要技术的主要优点和缺点。我们还将讨论对其在OT中应用的相关性的评估，从制造商的角度来看。本文的另一部分介绍了工业4.0的基本驱动因素和原理，提供了对AI在制造系统中的出现的见解，以及对AI所蕴含的新挑战的理解。提出了基于AI的生产监控，优化和控制技术，并对一些应用案例进行了深入分析。讨论了相关的技术，运营和安全挑战，然后更详细地介绍了这种过渡对当前安全实践的影响。该报告的最后部分进一步发展了对工业4.0安全挑战的愿景。它解决了分布式检测技术的编排方面，