SMS-I: Intelligent Security for Cyber–Physical Systems

Detalhes bibliográficos
Autor(a) principal: Maia, Eva
Data de Publicação: 2022
Outros Autores: Sousa, Norberto, Oliveira, Nuno, Wannous, Sinan, Sousa, Orlando, Praça, Isabel
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/21848
Resumo: Critical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.
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spelling SMS-I: Intelligent Security for Cyber–Physical SystemsCyber–physical systemsDigital forensicsCyber–physical systems forensicsMachine LearningRule miningSecurity incident responseCritical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.This research was funded by the Horizon 2020 Framework Programme under grant agreement No 832969. This output reflects the views only of the author(s), and the European Union cannot be held responsible for any use which may be made of the information contained therein. For more information on the project see: http://satie-h2020.eu/.MDPIRepositório Científico do Instituto Politécnico do PortoMaia, EvaSousa, NorbertoOliveira, NunoWannous, SinanSousa, OrlandoPraça, Isabel2023-01-25T11:11:34Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/21848eng10.3390/info13090403info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:18:10Zoai:recipp.ipp.pt:10400.22/21848Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:41:55.964042Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv SMS-I: Intelligent Security for Cyber–Physical Systems
title SMS-I: Intelligent Security for Cyber–Physical Systems
spellingShingle SMS-I: Intelligent Security for Cyber–Physical Systems
Maia, Eva
Cyber–physical systems
Digital forensics
Cyber–physical systems forensics
Machine Learning
Rule mining
Security incident response
title_short SMS-I: Intelligent Security for Cyber–Physical Systems
title_full SMS-I: Intelligent Security for Cyber–Physical Systems
title_fullStr SMS-I: Intelligent Security for Cyber–Physical Systems
title_full_unstemmed SMS-I: Intelligent Security for Cyber–Physical Systems
title_sort SMS-I: Intelligent Security for Cyber–Physical Systems
author Maia, Eva
author_facet Maia, Eva
Sousa, Norberto
Oliveira, Nuno
Wannous, Sinan
Sousa, Orlando
Praça, Isabel
author_role author
author2 Sousa, Norberto
Oliveira, Nuno
Wannous, Sinan
Sousa, Orlando
Praça, Isabel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Maia, Eva
Sousa, Norberto
Oliveira, Nuno
Wannous, Sinan
Sousa, Orlando
Praça, Isabel
dc.subject.por.fl_str_mv Cyber–physical systems
Digital forensics
Cyber–physical systems forensics
Machine Learning
Rule mining
Security incident response
topic Cyber–physical systems
Digital forensics
Cyber–physical systems forensics
Machine Learning
Rule mining
Security incident response
description Critical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
2023-01-25T11:11:34Z
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/21848
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