An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems

Mayra Macas, Mayra Macas, Chunming Wu, Walter Fuertes

2023

Abstract

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.

Paper Citation

in Harvard Style

Macas M., Wu C. and Fuertes W. (2023). An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS; ISBN 978-989-758-672-9, SciTePress, pages 566-577. DOI: 10.5220/0012264000003584

in Bibtex Style

@conference{dmmlacs23,
author={Mayra Macas and Chunming Wu and Walter Fuertes},
title={An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS},
year={2023},
pages={566-577},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012264000003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS
TI - An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems
SN - 978-989-758-672-9
AU - Macas M.
AU - Wu C.
AU - Fuertes W.
PY - 2023
SP - 566
EP - 577
DO - 10.5220/0012264000003584
PB - SciTePress