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.
Highlights
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, in SCADA networks
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
The unsupervised feature learning capability that makes it possible to learn important features from available SCADA network large data in order to deliver high anomaly detection rate contributes to the rising interest in deep learning approaches
Summary
ICS are used to be isolated from enterprise networks, making attacks against them difficult. These systems were using proprietary hardware, software and protocols. Many countermeasures are deployed to secure ICS networks, Intrusion and anomaly detection systems are important complementary security measures used to protect them. Various works are attempting to use deep learning for networks anomaly detection [8] [9] [10]. In this paper we are making a review of SCADA networks anomaly detection systems which are using deep feature learning approach. After some highlights on the concept of the unsupervised feature learning, the third section is dedicated to the review of different anomaly detection systems in SCADA networks using deep unsupervised feature learning. We draw a conclusion of the review
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