A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes
Ensuring cybersecurity and health management is a fundamental requirement in modern chemical industry plants operating under the Industry 4.0 framework. Traditionally, these two concerns have been addressed independently, despite sharing multiple underlying elements which suggest the viability of a...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-0825/14/7/329 |
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| author | Adrián Rodríguez Ramos Pedro Juan Rivera Torres Orestes Llanes-Santiago |
| author_facet | Adrián Rodríguez Ramos Pedro Juan Rivera Torres Orestes Llanes-Santiago |
| author_sort | Adrián Rodríguez Ramos |
| collection | DOAJ |
| description | Ensuring cybersecurity and health management is a fundamental requirement in modern chemical industry plants operating under the Industry 4.0 framework. Traditionally, these two concerns have been addressed independently, despite sharing multiple underlying elements which suggest the viability of a unified detection and localization solution. This study introduces a computational intelligence framework based on fuzzy techniques, which allows for the early identification and precise localization of both faults and cyberattacks, along with the capability to recognize previously unseen events during runtime. Once new events are identified and classified, the training database is updated, creating a mechanism for continuous learning. This integrated approach simplifies the computational complexity of supervisory systems and enhances collaboration between the Operational Technology and Information Technology teams within chemical plants. The proposed methodology demonstrates strong robustness and reliability, even in complex conditions characterized by noisy measurements and disturbances, achieving outstanding performance due to its excellent discrimination capabilities. |
| format | Article |
| id | doaj-art-fc5e390c30564b3a87adb43d5fb6fa6d |
| institution | Matheson Library |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-fc5e390c30564b3a87adb43d5fb6fa6d2025-07-25T13:08:48ZengMDPI AGActuators2076-08252025-07-0114732910.3390/act14070329A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical ProcessesAdrián Rodríguez Ramos0Pedro Juan Rivera Torres1Orestes Llanes-Santiago2Laboratório LEMA-LEMEC, Instituto Politécnico-Universidade do Estado do Rio de Janeiro, Rua Bonfim 25, Vila Amélia, Nova Friburgo, Rio de Janeiro CEP 28625-570, BrazilDepartamento de Informática y Automática, Universidad de Salamanca, Patio de las Escuelas 1, 37008 Salamanca, SpainPrograma de Pós-Graduação em Modelagem Computacional, Instituto Politécnico-Universidade do Estado do Rio de Janeiro, Rua Bonfim 25, Vila Amélia, Nova Friburgo, Rio de Janeiro CEP 28625-570, BrazilEnsuring cybersecurity and health management is a fundamental requirement in modern chemical industry plants operating under the Industry 4.0 framework. Traditionally, these two concerns have been addressed independently, despite sharing multiple underlying elements which suggest the viability of a unified detection and localization solution. This study introduces a computational intelligence framework based on fuzzy techniques, which allows for the early identification and precise localization of both faults and cyberattacks, along with the capability to recognize previously unseen events during runtime. Once new events are identified and classified, the training database is updated, creating a mechanism for continuous learning. This integrated approach simplifies the computational complexity of supervisory systems and enhances collaboration between the Operational Technology and Information Technology teams within chemical plants. The proposed methodology demonstrates strong robustness and reliability, even in complex conditions characterized by noisy measurements and disturbances, achieving outstanding performance due to its excellent discrimination capabilities.https://www.mdpi.com/2076-0825/14/7/329cybersecurityhealth managementintegrated systemscontinuous learningcomputational intelligence |
| spellingShingle | Adrián Rodríguez Ramos Pedro Juan Rivera Torres Orestes Llanes-Santiago A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes Actuators cybersecurity health management integrated systems continuous learning computational intelligence |
| title | A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes |
| title_full | A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes |
| title_fullStr | A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes |
| title_full_unstemmed | A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes |
| title_short | A Computational Intelligence-Based Proposal for Cybersecurity and Health Management with Continuous Learning in Chemical Processes |
| title_sort | computational intelligence based proposal for cybersecurity and health management with continuous learning in chemical processes |
| topic | cybersecurity health management integrated systems continuous learning computational intelligence |
| url | https://www.mdpi.com/2076-0825/14/7/329 |
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