Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations
The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example,...
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| Format: | Article |
| Language: | English |
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Editorial Neogranadina
2019-11-01
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| Series: | Ciencia e Ingeniería Neogranadina |
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| Online Access: | http://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4236 |
| _version_ | 1797268103679180800 |
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| author | Erwin Alexander Leal Piedrahita |
| author_facet | Erwin Alexander Leal Piedrahita |
| author_sort | Erwin Alexander Leal Piedrahita |
| collection | DOAJ |
| description |
The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example, in the field of security, several academic works have shown that the same attacks used in computer networks (DoS, Sniffing, Tampering, Spoffing among others), can also compromise the operation of a substation. This article evaluates the applicability of hierarchical clustering algorithms and statistical type descriptors (averages), in the identification of anomalous patterns of traffic in communication networks for power substations based on the IEC 61850 standard. The results obtained show that, using a hierarchical algorithm with Euclidean distance proximity criterion and simple link grouping method, a correct classification is achieved in the following operation scenarios: 1) Normal traffic, 2) IED disconnection, 3) Network discovery attack, 4) DoS attack, 5) IED spoofing attack and 6) Failure on the high voltage line. In addition, the descriptors used for the classification proved equally effective with other unsupervised clustering techniques such as K-means (partitional-type clustering), or LAMDA (diffuse-type clustering).
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| first_indexed | 2024-03-07T14:17:48Z |
| format | Article |
| id | doaj.art-bce7d352e528446da2fe798d03dfae9b |
| institution | Directory Open Access Journal |
| issn | 0124-8170 1909-7735 |
| language | English |
| last_indexed | 2024-04-25T01:27:10Z |
| publishDate | 2019-11-01 |
| publisher | Editorial Neogranadina |
| record_format | Article |
| series | Ciencia e Ingeniería Neogranadina |
| spelling | doaj.art-bce7d352e528446da2fe798d03dfae9b2024-03-08T17:04:03ZengEditorial NeogranadinaCiencia e Ingeniería Neogranadina0124-81701909-77352019-11-01301Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power SubstationsErwin Alexander Leal Piedrahita0Universidad de Antioquia The IEC 61850 standard has contributed significantly to the substation management and automation process by incorporating the advantages of communications networks into the operation of power substations. However, this modernization process also involves new challenges in other areas. For example, in the field of security, several academic works have shown that the same attacks used in computer networks (DoS, Sniffing, Tampering, Spoffing among others), can also compromise the operation of a substation. This article evaluates the applicability of hierarchical clustering algorithms and statistical type descriptors (averages), in the identification of anomalous patterns of traffic in communication networks for power substations based on the IEC 61850 standard. The results obtained show that, using a hierarchical algorithm with Euclidean distance proximity criterion and simple link grouping method, a correct classification is achieved in the following operation scenarios: 1) Normal traffic, 2) IED disconnection, 3) Network discovery attack, 4) DoS attack, 5) IED spoofing attack and 6) Failure on the high voltage line. In addition, the descriptors used for the classification proved equally effective with other unsupervised clustering techniques such as K-means (partitional-type clustering), or LAMDA (diffuse-type clustering). http://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4236HierarchicalclusteringunsupervisedIEC 61850traffic detectionpower substation |
| spellingShingle | Erwin Alexander Leal Piedrahita Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations Ciencia e Ingeniería Neogranadina Hierarchical clustering unsupervised IEC 61850 traffic detection power substation |
| title | Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations |
| title_full | Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations |
| title_fullStr | Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations |
| title_full_unstemmed | Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations |
| title_short | Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations |
| title_sort | hierarchical clustering for anomalous traffic conditions detection in power substations |
| topic | Hierarchical clustering unsupervised IEC 61850 traffic detection power substation |
| url | http://revistasunimilitareduco.biteca.online/index.php/rcin/article/view/4236 |
| work_keys_str_mv | AT erwinalexanderlealpiedrahita hierarchicalclusteringforanomaloustrafficconditionsdetectioninpowersubstations |