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Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

Raogo Kabore 1 Adlès Kouassi 1 Rodrigue N’goran 1 Olivier Asseu 1 Yvon Kermarrec 2, 3 Philippe Lenca 4, 2
3 Lab-STICC_IMTA_CID_IRIS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 Lab-STICC_IMTA_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks.
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https://hal-imt-atlantique.archives-ouvertes.fr/hal-03174461
Contributor : Philippe Lenca <>
Submitted on : Friday, March 19, 2021 - 11:12:53 AM
Last modification on : Monday, April 12, 2021 - 3:50:03 PM

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Raogo Kabore, Adlès Kouassi, Rodrigue N’goran, Olivier Asseu, Yvon Kermarrec, et al.. Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach. Engineering, Scientific Research, 2021, 13, pp.30 - 44. ⟨10.4236/eng.2021.131003⟩. ⟨hal-03174461⟩

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