工业互联网安全是制造强国和网络强国建设的基石，深度学习因其具有表达能力强、适应性好、可移植性高等优点而可支持“智能自主式”工业互联网安全体系与方法构建，因此促进深度学习与工业互联网安全的融合创新具有鲜明价值。本文从产业宏观、安全技术、深度学习系统等角度全面分析了发展需求，从设备层、控制层、网络层、应用层、数据层的角度剖析了深度学习应用于工业互联网安全的发展现状；阐述了工业互联网深度学习应用在模型训练、模型预测方面的安全挑战，前瞻研判了未来研究的重点方向，如深度神经网络可解释性、样本收集和计算成本、样本集不均衡、模型结果可靠性、可用性与安全性平衡等。研究建议，在总体安全策略方面，深化促进两者的融合发展，建立动态的纵深防御体系；在技术攻关研究方面，采用应用驱动和前沿探索相结合的攻关方式，加快领域关键技术问题的攻关突破；在政策支持与引导方面，合理增加交叉领域的资源投入，建立“产学研”联合研发与应用的生态体系。

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 industrial Internet security system and method that is intelligent and autonomous. 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 security primarily lie in model training and prediction, and key research directions include interpretability of deep neural networks, cost control of sample collection and calculation, imbalance of sample sets, reliability of model results, tradeoff between availability and security. Furthermore, some suggestion 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 interdisciplinary fields to establish an industry–university–research institute joint research ecosystem.