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Tytuł:
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Toward the Integration of Cyber and Physical Security Monitoring Systems for Critical Infrastructures.
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Autorzy:
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Fausto A; DITEN Department, University of Genoa, 16145 Genoa, Italy.
Gaggero GB; DITEN Department, University of Genoa, 16145 Genoa, Italy.
Patrone F; DITEN Department, University of Genoa, 16145 Genoa, Italy.
Girdinio P; DITEN Department, University of Genoa, 16145 Genoa, Italy.
Marchese M; DITEN Department, University of Genoa, 16145 Genoa, Italy.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Oct 20; Vol. 21 (21). Date of Electronic Publication: 2021 Oct 20.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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MeSH Terms:
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Computer Security*
Information Systems*
Algorithms ; Computers ; Humans
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References:
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J Signal Process Syst. 2014;74(3):323-339. (PMID: 24683434)
ACM Comput Surv. 2018;51:. (PMID: 31092968)
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Contributed Indexing:
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Keywords: anomaly detection; critical infrastructure; cybersecurity; machine learning; physical security
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Entry Date(s):
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Date Created: 20211113 Date Completed: 20211116 Latest Revision: 20240404
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Update Code:
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20240404
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PubMed Central ID:
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PMC8588483
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DOI:
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10.3390/s21216970
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PMID:
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34770277
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Critical Infrastructures (CIs) are sensible targets. They could be physically damaged by natural or human actions, causing service disruptions, economic losses, and, in some extreme cases, harm to people. They, therefore, need a high level of protection against possible unintentional and intentional events. In this paper, we show a logical architecture that exploits information from both physical and cybersecurity systems to improve the overall security in a power plant scenario. We propose a Machine Learning (ML)-based anomaly detection approach to detect possible anomaly events by jointly correlating data related to both the physical and cyber domains. The performance evaluation showed encouraging results-obtained by different ML algorithms-which highlights how our proposed approach is able to detect possible abnormal situations that could not have been detected by using only information from either the physical or cyber domain.