Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification
There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selec...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universidad de Medellín
2021-03-01
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| Series: | Revista Ingenierías Universidad de Medellín |
| Subjects: | |
| Online Access: | https://revistas.udem.edu.co/index.php/ingenierias/article/view/3009 |
| _version_ | 1818330213473320960 |
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| author | Angela María Vargas Arcila Juan Carlos Corrales Muñoz Alvaro Rendon Gallon Araceli Sanchis |
| author_facet | Angela María Vargas Arcila Juan Carlos Corrales Muñoz Alvaro Rendon Gallon Araceli Sanchis |
| author_sort | Angela María Vargas Arcila |
| collection | DOAJ |
| description | There are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes. |
| first_indexed | 2024-12-13T13:00:23Z |
| format | Article |
| id | doaj.art-2a8b506c682b4cccace1644869012f82 |
| institution | Directory Open Access Journal |
| issn | 1692-3324 2248-4094 |
| language | English |
| last_indexed | 2024-12-13T13:00:23Z |
| publishDate | 2021-03-01 |
| publisher | Universidad de Medellín |
| record_format | Article |
| series | Revista Ingenierías Universidad de Medellín |
| spelling | doaj.art-2a8b506c682b4cccace1644869012f822022-12-21T23:45:03ZengUniversidad de MedellínRevista Ingenierías Universidad de Medellín1692-33242248-40942021-03-012038678510.22395/rium.v20n38a4Selection of Online Network Traffic Discriminators for on-the-Fly Traffic ClassificationAngela María Vargas Arcila0https://orcid.org/0000-0002-4313-5445Juan Carlos Corrales Muñoz1https://orcid.org/0000-0002-5608-9097Alvaro Rendon Gallon2https://orcid.org/0000-0002-2935-7316Araceli Sanchis3https://orcid.org/0000-0002-1429-4092Universidad del CaucaUniversidad del CaucaUniversidad del CaucaUniversidad Carlos III de MadridThere are several techniques to select a set of traffic features for traffic classification. However, most studies ignore the domain knowledge where traffic analysis or classification is performed and do not consider the always moving information carried in the networks. This paper describes a selection process of online network-traffic discriminators. We obtained 24 traffic features that can be processed on the fly and propose them as a base attribute set for future domain-aware online analysis, processing, or classification. For the selection of a set of traffic discriminators, and to avoid the inconveniences mentioned, we carried out three steps. The first step is a context knowledge-based manual selection of traffic features that meet the condition of being obtained on the fly from the flow. The second step is focused on the quality analysis of previously selected attributes to ensure the relevance of each one when performing a traffic classification. In the third step, the implementation of several incremental learning algorithms verified the usefulness of such attributes in online traffic classification processes.https://revistas.udem.edu.co/index.php/ingenierias/article/view/3009incremental learningnetwork traffic classificationonline classification |
| spellingShingle | Angela María Vargas Arcila Juan Carlos Corrales Muñoz Alvaro Rendon Gallon Araceli Sanchis Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification Revista Ingenierías Universidad de Medellín incremental learning network traffic classification online classification |
| title | Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification |
| title_full | Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification |
| title_fullStr | Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification |
| title_full_unstemmed | Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification |
| title_short | Selection of Online Network Traffic Discriminators for on-the-Fly Traffic Classification |
| title_sort | selection of online network traffic discriminators for on the fly traffic classification |
| topic | incremental learning network traffic classification online classification |
| url | https://revistas.udem.edu.co/index.php/ingenierias/article/view/3009 |
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