  Anomaly detection is critical for the secure and reliable operation of
industrial control systems. As our reliance on such complex cyber-physical
systems grows, it becomes paramount to have automated methods for detecting
anomalies, preventing attacks, and responding intelligently. {This paper
presents a novel deep generative model to meet this need. The proposed model
follows a variational autoencoder architecture with a convolutional encoder and
decoder to extract features from both spatial and temporal dimensions.
Additionally, we incorporate an attention mechanism that directs focus towards
specific regions, enhancing the representation of relevant features and
improving anomaly detection accuracy. We also employ a dynamic threshold
approach leveraging the reconstruction probability and make our source code
publicly available to promote reproducibility and facilitate further research.
Comprehensive experimental analysis is conducted on data from all six stages of
the Secure Water Treatment (SWaT) testbed, and the experimental results
demonstrate the superior performance of our approach compared to several
state-of-the-art baseline techniques.
