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The Industrial Internet of Things (IIoT), also known as Industry 4.0, empowers manufacturing and production processes by leveraging automation and Internet of Things (IoT) technologies. In IIoT, the information communication technologies enabled by IoT could greatly improve the efficiency and timeliness of information exchanges between both vertical and horizontal system integrations. Likewise, machine learning algorithms, particularly Deep Reinforcement Learning (DRL), are viable for assisting in automated control of complex IIoT systems, with the support of distributed edge computing infrastructure. Despite noticeable performance improvements, the security threats brought by massive interconnections in IoT and the vulnerabilities of deep neural networks used in DRL must be thoroughly investigated and mitigated before widespread deployment. Thus, in this paper we first design a DRL-based controller that …