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Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations

Alqudhaibi, Adel ; Albarrak, Majed ; Aloseel, Abdulmohsan ; Jagtap, Sandeep LU orcid and Salonitis, Konstantinos (2023) In Sensors 23(9).
Abstract
In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The... (More)
In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future. (Less)
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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
in
Sensors
volume
23
issue
9
article number
4539
publisher
MDPI AG
external identifiers
  • pmid:37177743
  • scopus:85159233603
ISSN
1424-8220
DOI
10.3390/s23094539
language
English
LU publication?
no
id
7a8f8491-3e8c-42ee-a594-83d4cf14f48e
date added to LUP
2023-09-06 10:55:03
date last changed
2024-03-08 09:24:29
@article{7a8f8491-3e8c-42ee-a594-83d4cf14f48e,
  abstract     = {{In Industry 4.0, manufacturing and critical systems require high levels of flexibility and resilience for dynamic outcomes. Industrial Control Systems (ICS), specifically Supervisory Control and Data Acquisition (SCADA) systems, are commonly used for operation and control of Critical Infrastructure (CI). However, due to the lack of security controls, standards, and proactive security measures in the design of these systems, they have security risks and vulnerabilities. Therefore, efficient and effective security solutions are needed to secure the conjunction between CI and I4.0 applications. This paper predicts potential cyberattacks and threats against CI systems by considering attacker motivations and using machine learning models. The approach presents a novel cybersecurity prediction technique that forecasts potential attack methods, depending on specific CI and attacker motivations. The proposed model’s accuracy in terms of False Positive Rate (FPR) reached 66% with the trained and test datasets. This proactive approach predicts potential attack methods based on specific CI and attacker motivations, and doubling the trained data sets will improve the accuracy of the proposed model in the future.}},
  author       = {{Alqudhaibi, Adel and Albarrak, Majed and Aloseel, Abdulmohsan and Jagtap, Sandeep and Salonitis, Konstantinos}},
  issn         = {{1424-8220}},
  language     = {{eng}},
  number       = {{9}},
  publisher    = {{MDPI AG}},
  series       = {{Sensors}},
  title        = {{Predicting Cybersecurity Threats in Critical Infrastructure for Industry 4.0: A Proactive Approach Based on Attacker Motivations}},
  url          = {{http://dx.doi.org/10.3390/s23094539}},
  doi          = {{10.3390/s23094539}},
  volume       = {{23}},
  year         = {{2023}},
}