ResearchSpace

Hydraulic data preprocessing for machine learning-based intrusion detection in cyber-physical systems

Show simple item record

dc.contributor.author Mboweni, IV
dc.contributor.author Ramotsoela, DT
dc.contributor.author Abu-Mahfouz, Adnan MI
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
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


Files in this item

This item appears in the following Collection(s)

Show simple item record