In recent years, the rapid development and wide application of advanced technologies have profoundly impacted industrial manufacturing, leading to smart manufacturing (SM). However, the Industrial IoT (IIoT)-based manufacturing systems are now one of the top industries targeted by a variety of attacks. In this research, we propose detecting Cyberattacks in Industrial Control Systems using Anomaly Detection. An anomaly detection architecture for the IIoT-based SM is proposed to deploy one of the top most concerned networking technique - a Federated Learning architecture - that can detect anomalies for time series data typically running inside an industrial system. The architecture achieves higher detection performance compared to the current detection solution for time series data. It also shows the feasibility and efficiency to be deployed on top of edge computing hardware of an IIoT-based SM that can save 35% of bandwidth consumed in the transmission link between the edge and the cloud. At the expense, the architecture needs to trade off with the computing resource consumed at edge devices for implementing the detection task. However, findings in maximal CPU usage of 85% and average Memory usage of 37% make this architecture totally realizable in an IIoT-based SM.

近年来，先进技术的快速发展和广泛应用，对工业制造产生了深远的影响，智能制造（SM）由此而生。然而，基于工业物联网 (IIoT) 的制造系统现在是各种攻击的主要目标行业之一。在这项研究中，我们建议使用异常检测来检测工业控制系统中的网络攻击。提出了一种用于基于 IIoT 的 SM 的异常检测架构，以部署最受关注的网络技术之一——联合学习架构——该架构可以检测通常在工业系统内运行的时间序列数据的异常。与当前的时间序列数据检测解决方案相比，该架构实现了更高的检测性能。它还显示了部署在基于 IIoT 的 SM 的边缘计算硬件之上的可行性和效率，可以节省 35% 的边缘和云之间传输链路中消耗的带宽。代价是，架构需要与边缘设备消耗的计算资源进行权衡，以实现检测任务。然而，85% 的最大 CPU 使用率和 37% 的平均内存使用率的发现使该架构在基于 IIoT 的 SM 中完全可以实现。