Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces
High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10064283/ |
| _version_ | 1797869081905332224 |
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| author | Juan Ramon Feijoo-Martinez Alicia Guerrero-Curieses Francisco Gimeno-Blanes Mario Castro-Fernandez Jose Luis Rojo-Alvarez |
| author_facet | Juan Ramon Feijoo-Martinez Alicia Guerrero-Curieses Francisco Gimeno-Blanes Mario Castro-Fernandez Jose Luis Rojo-Alvarez |
| author_sort | Juan Ramon Feijoo-Martinez |
| collection | DOAJ |
| description | High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe high-severity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks. |
| first_indexed | 2024-04-10T00:05:56Z |
| format | Article |
| id | doaj.art-baef8933479c4603ba54db27ae5caea1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T00:05:56Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-baef8933479c4603ba54db27ae5caea12023-03-16T23:00:27ZengIEEEIEEE Access2169-35362023-01-0111237542377010.1109/ACCESS.2023.325510110064283Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent SpacesJuan Ramon Feijoo-Martinez0https://orcid.org/0000-0001-9685-4032Alicia Guerrero-Curieses1https://orcid.org/0000-0001-7403-165XFrancisco Gimeno-Blanes2https://orcid.org/0000-0002-2727-2132Mario Castro-Fernandez3Jose Luis Rojo-Alvarez4https://orcid.org/0000-0003-0426-8912Red Eléctrica de España, Alcobendas, Madrid, SpainDepartment of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Fuenlabrada, Madrid, SpainD!lemmaLab Ldt Startup, Fuenlabrada, Madrid, SpainRed Eléctrica de España, Alcobendas, Madrid, SpainDepartment of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Fuenlabrada, Madrid, SpainHigh-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe high-severity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks.https://ieeexplore.ieee.org/document/10064283/Telecommunication securityintrusion detectiondeep learninghigh powerpower communicationlatent variables |
| spellingShingle | Juan Ramon Feijoo-Martinez Alicia Guerrero-Curieses Francisco Gimeno-Blanes Mario Castro-Fernandez Jose Luis Rojo-Alvarez Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces IEEE Access Telecommunication security intrusion detection deep learning high power power communication latent variables |
| title | Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces |
| title_full | Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces |
| title_fullStr | Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces |
| title_full_unstemmed | Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces |
| title_short | Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces |
| title_sort | cybersecurity alert prioritization in a critical high power grid with latent spaces |
| topic | Telecommunication security intrusion detection deep learning high power power communication latent variables |
| url | https://ieeexplore.ieee.org/document/10064283/ |
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