Summary of An Attention-based Deep Generative Model For Anomaly Detection in Industrial Control Systems, by Mayra Macas et al.
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems
by Mayra Macas, Chunming Wu, Walter Fuertes
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel deep generative model for anomaly detection in industrial control systems leverages a variational autoencoder architecture with convolutional encoder and decoder to extract features from spatial and temporal dimensions. The model incorporates an attention mechanism, focusing on specific regions and enhancing feature representation, leading to improved accuracy. A dynamic threshold approach is also employed, using reconstruction probability, and the source code is made publicly available for reproducibility and further research. Experimental results demonstrate superior performance compared to state-of-the-art baseline techniques on the Secure Water Treatment (SWaT) testbed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial control systems need anomaly detection methods to stay secure and reliable. This paper presents a new way to detect anomalies using deep learning. The approach uses a special type of autoencoder that looks at both space and time to learn features from data. It also has an attention mechanism, which helps focus on important parts of the data. This leads to better anomaly detection accuracy. The code is shared publicly so others can use it and build upon this research. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Autoencoder » Decoder » Deep learning » Encoder » Generative model » Probability » Variational autoencoder
