Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system
Vulnerabilities in the Industrial Control Systems (ICSs) and Supervisory Control and Data Acquisition (SCADA) systems are constantly increasing as these systems incorporate innovative technologies such as the Internet of Things (IoT). As a result of these advancements, the SCADA system became more...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/98761/7/98761_Applications%20of%20deep%20learning%20algorithms.pdf http://irep.iium.edu.my/98761/ https://www.sciencedirect.com/journal/cleaner-engineering-and-technology |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | Vulnerabilities in the Industrial Control Systems (ICSs) and Supervisory Control and Data Acquisition (SCADA)
systems are constantly increasing as these systems incorporate innovative technologies such as the Internet of
Things (IoT). As a result of these advancements, the SCADA system became more efficient, simpler to operate,
but more exposed to cyber-attacks. A well-planned cyber-attack against SCADA systems can have catastrophic
consequences, including physical property damage and even fatalities. To secure these critical infrastructures,
security measures should be examined and implemented. These methods could be hardware-based, such as
Intrusion Detection Systems (IDS), software-based, or managerial-based. In this paper, we have examined and
presented the most recent research on developing robust IDSs using Deep Learning (DL) algorithms, including
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Stacked Autoencoders (SAE), and
Deep Belief Networks (DBN). For each algorithm, prior works have been identified, examined, and described
based on their conceptual similarities. A comparison between different IDS-DL models is provided based on their
performance metrics. Because data is such a crucial component of the training and evaluation of IDS-DL models,
some of the most utilized network datasets in DL are discussed. The challenges facing DL applications in IDS
development are also discussed, as well as future research direction and recommendations. |
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