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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2022
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my.iium.irep.987612022-07-14T06:28:25Z http://irep.iium.edu.my/98761/ Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system Balla, Asaad Habaebi, Mohamed Hadi Islam, Md. Rafiqul Mubarak, Sinil TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices 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. Elsevier 2022 Article PeerReviewed application/pdf en http://irep.iium.edu.my/98761/7/98761_Applications%20of%20deep%20learning%20algorithms.pdf Balla, Asaad and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul and Mubarak, Sinil (2022) Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system. Cleaner Engineering and Technology, 9. pp. 1-10. ISSN 2666-7908 https://www.sciencedirect.com/journal/cleaner-engineering-and-technology 10.1016/j.clet.2022.100532 |
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TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
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TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Balla, Asaad Habaebi, Mohamed Hadi Islam, Md. Rafiqul Mubarak, Sinil Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| description |
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. |
| format |
Article |
| author |
Balla, Asaad Habaebi, Mohamed Hadi Islam, Md. Rafiqul Mubarak, Sinil |
| author_facet |
Balla, Asaad Habaebi, Mohamed Hadi Islam, Md. Rafiqul Mubarak, Sinil |
| author_sort |
Balla, Asaad |
| title |
Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| title_short |
Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| title_full |
Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| title_fullStr |
Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| title_full_unstemmed |
Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| title_sort |
applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system |
| publisher |
Elsevier |
| publishDate |
2022 |
| url |
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 |
| _version_ |
1738510133871247360 |
| score |
13.211869 |