工业互联网供应链在促进企业生产效率提升、降低企业经营成本的同时，扩大了工业控制系统、生产设备等的受攻击面，引入了新的安全风险。针对工业互联网供应链安全发展问题，本文描述了工业互联网供应链发展形势，梳理了工业互联网供应链存在的典型安全问题，分析了我国工业互联网供应链面临的安全挑战。研究提出了我国工业互联网供应链安全的创新发展路径：发挥制造产业种类齐全的良好优势，加强工业互联网安全知识产权布局，加速实现信息创新产业体系建设，提升工业互联网供应链技术安全保障能力，重视工业互联网供应链渠道安全，鼓励上下游企业良性协同发展，优化工业互联网供应链安全发展环境。

Industrial Internet security is crucial for strengthening the manufacturing and network sectors of China. Deep learning, owing to its strong expression ability, good adaptability, and high portability, can support the establishment of an intelligent and autonomous industrial Internet security system and method. Therefore, it is of great value to promote the integrated innovation of deep learning and industrial Internet security. In this study, we analyze the development demand for industrial Internet security from the perspective of macro industrial environment, security technology, and deep learning system, and summarize the application status of deep learning to industrial Internet security in terms of device, control, network, application, and data layers. The security challenges faced by deep learning application to industrial Internet primarily lie in model training and prediction. Furthermore, we identify key research directions including interpretability of deep neural networks, cost control of sample collection and calculation, imbalance of sample sets, reliability of model results, and tradeoff between availability and security. Finally, some suggestions are proposed: A dynamic defense system in depth should be established in terms of overall security strategy; an application-driven and frontier exploration integrated method should be adopted to achieve breakthroughs regarding key technologies; and resources input should be raised for such interdisciplinary fields to establish an industry–university–research institute joint research ecosystem.