dc.contributor.author |
Mboweni, IV
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|
dc.contributor.author |
Ramotsoela, DT
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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|
dc.date.accessioned |
2023-12-08T09:22:03Z |
|
dc.date.available |
2023-12-08T09:22:03Z |
|
dc.date.issued |
2023-04 |
|
dc.identifier.citation |
Mboweni, I., Ramotsoela, D. & Abu-Mahfouz, A.M. 2023. Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems. <i>Mathematics, 11(8).</i> http://hdl.handle.net/10204/13369 |
en_ZA |
dc.identifier.issn |
2227-7390 |
|
dc.identifier.uri |
https://doi.org/10.3390/math11081846
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|
dc.identifier.uri |
http://hdl.handle.net/10204/13369
|
|
dc.description.abstract |
The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/2227-7390/11/8/1846 |
en_US |
dc.source |
Mathematics, 11(8) |
en_US |
dc.subject |
Critical infrastructure |
en_US |
dc.subject |
Critical water system infrastructure |
en_US |
dc.subject |
Cyber-physical systems |
en_US |
dc.subject |
Data preprocessing |
en_US |
dc.subject |
Industrial control |
en_US |
dc.subject |
Intrusion detection systems |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Water treatment system |
en_US |
dc.title |
Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
21 |
en_US |
dc.description.note |
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Mboweni, I., Ramotsoela, D., & Abu-Mahfouz, A. M. (2023). Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems. <i>Mathematics, 11(8)</i>, http://hdl.handle.net/10204/13369 |
en_ZA |
dc.identifier.chicagocitation |
Mboweni, IV, DT Ramotsoela, and Adnan MI Abu-Mahfouz "Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems." <i>Mathematics, 11(8)</i> (2023) http://hdl.handle.net/10204/13369 |
en_ZA |
dc.identifier.vancouvercitation |
Mboweni I, Ramotsoela D, Abu-Mahfouz AM. Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems. Mathematics, 11(8). 2023; http://hdl.handle.net/10204/13369. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Mboweni, IV
AU - Ramotsoela, DT
AU - Abu-Mahfouz, Adnan MI
AB - The protection of critical infrastructure such as water treatment and water distribution systems is crucial for a functioning economy. The use of cyber-physical systems in these systems presents numerous vulnerabilities to attackers. To enhance security, intrusion detection systems play a crucial role in limiting damage from successful attacks. Machine learning can enhance security by analysing data patterns, but several attributes of the data can negatively impact the performance of the machine learning model. Data in critical water system infrastructure can be difficult to work with due to their complexity, variability, irregularities, and sensitivity. The data involve various measurements and can vary over time due to changes in environmental conditions and operational changes. Irregular patterns and small changes can have significant impacts on analysis and decision making, requiring effective data preprocessing techniques to handle the complexities and ensure accurate analysis. This paper explores data preprocessing techniques using a water treatment system dataset as a case study and provides preprocessing techniques specific to processing data in industrial control to yield a more informative dataset. The results showed significant improvement in accuracy, F1 score, and time to detection when using the preprocessed dataset.
DA - 2023-04
DB - ResearchSpace
DP - CSIR
J1 - Mathematics, 11(8)
KW - Critical infrastructure
KW - Critical water system infrastructure
KW - Cyber-physical systems
KW - Data preprocessing
KW - Industrial control
KW - Intrusion detection systems
KW - Machine learning
KW - Water treatment system
LK - https://researchspace.csir.co.za
PY - 2023
SM - 2227-7390
T1 - Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems
TI - Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems
UR - http://hdl.handle.net/10204/13369
ER -
|
en_ZA |
dc.identifier.worklist |
27204 |
en_US |