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Applications of deep learning algorithms for supervisory control and data acquisition intrusion detection system

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

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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.

Item Type: Article (Review)
Uncontrolled Keywords: Deep learning; Intrusion detection system; SCADA; Cyber-physical system; Cyber security
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr. Mohamed Hadi Habaebi
Date Deposited: 14 Jul 2022 08:48
Last Modified: 14 Jul 2022 14:28
URI: http://irep.iium.edu.my/id/eprint/98761

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