Industrial control systems often contain sensor and actuator devices, which provide monitoring data in the form of time series, such as bridge vibrations, water distribution systems, and human physiological data. This thesis proposes an anomaly detection model based on an autoencoder that can consider time-series relations of the data. Moreover, the quality of the decoder output is further improved by adding a residual produced by an extra generator and discriminator. The proposed autoencoder-GAN model and detection algorithm not only improved the performance but also made the training process of GAN easier. The proposed deep learning model with the anomaly detection algorithm has been shown to achieve better results on the SWaT, BATADAL, and Rare Event Classification datasets over common methods.