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ZENODO
Other literature type . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2024
License: CC BY
Data sources: Datacite
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AI-Enhanced Critical Infrastructure Defense: Protecting SCADA and ICS Networks Through Intelligent Monitoring

Authors: Sarraf, Gaurav;

AI-Enhanced Critical Infrastructure Defense: Protecting SCADA and ICS Networks Through Intelligent Monitoring

Abstract

Critical Infrastructure with AI Enhancement Defense is focused on protecting the types of Industrial Control Systems (ICS) or Supervisory Control and Data Acquisition (SCADA), vital to the functioning of utilities, water systems and healthcare systems as well as transportation networks. These systems are vulnerable to advanced cyberattacks due to digital transformation and integration with IT networks. These systems are at risk of highly evolved cyberattacks due to digital transformation and connections with IT networks. The paper is a discussion of growing vulnerability of SCADA and ICS and the emergence of a new role of Artificial Intelligence (AI) in improving defense strategies. The traditional approaches have miserably failed against APTs, zero-day vulnerabilities, phishing, and malware. The application of artificial intelligence (AI) in threat detection and prediction intelligence, and automated response are discussed as a potential solution to identify and prevent threats in real-time. It is revealed in the discussion that defense-in-depth, which includes segmentation, authentication, and layered monitoring with the assistance of AI insights are significant. The literature and findings presented in case studies indicate how AI can be used to transform cybersecurity through the study of evolving attack vectors. This will be more robust and effective in guarding critical infrastructure against high-level cyber threats.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green