Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

Anomaly Detection in Industrial Control Networks Using Hybrid LDA - Autoencoder Based Models

Authors
Hua Zhang, Shixiang Zhu, Jun Zhao, Minghui Gao, Zheng Shou, Ye Liang
Corresponding Author
Hua Zhang
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.10How to use a DOI?
Keywords
Anomaly detection; Industrial Control Networks; LDA; Autoencoder.
Abstract

This paper introduces a hybrid model that combines Latent Dirichlet Allocation (LDA) model with autoencoder to detect anomalies in Industrial Control Networks. The autoencoder provides a low-dimensional embedding for the input data, whose subsequent distribution is captured by the LDA model. The autoencoder thus acts as a trainable feature extractor while the LDA model captures the group structure of the data. This new approach potentially completes the strength of signature-based and anomaly-based methods.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.10How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hua Zhang
AU  - Shixiang Zhu
AU  - Jun Zhao
AU  - Minghui Gao
AU  - Zheng Shou
AU  - Ye Liang
PY  - 2016/11
DA  - 2016/11
TI  - Anomaly Detection in Industrial Control Networks Using Hybrid LDA - Autoencoder Based Models
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 53
EP  - 58
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.10
DO  - https://doi.org/10.2991/aiea-16.2016.10
ID  - Zhang2016/11
ER  -