Explainable Internet Traffic Classification
The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/10/4697 |
| _version_ | 1797533324561874944 |
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| author | Christian Callegari Pietro Ducange Michela Fazzolari Massimo Vecchio |
| author_facet | Christian Callegari Pietro Ducange Michela Fazzolari Massimo Vecchio |
| author_sort | Christian Callegari |
| collection | DOAJ |
| description | The problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method. |
| first_indexed | 2024-03-10T11:12:57Z |
| format | Article |
| id | doaj.art-1c5363297b9e4afe90f90432cb7bdf0f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:12:57Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1c5363297b9e4afe90f90432cb7bdf0f2023-11-21T20:36:21ZengMDPI AGApplied Sciences2076-34172021-05-011110469710.3390/app11104697Explainable Internet Traffic ClassificationChristian Callegari0Pietro Ducange1Michela Fazzolari2Massimo Vecchio3Quantavis s.r.l., 56126 Pisa, ItalyDepterment of Information Engineering, University of Pisa, 56126 Pisa, ItalyIstituto di Informatica e Telematica—CNR, 56124 Pisa, ItalyOpenIoT Research Unit, FBK, 38123 Trento, ItalyThe problem analyzed in this paper deals with the classification of Internet traffic. During the last years, this problem has experienced a new hype, as classification of Internet traffic has become essential to perform advanced network management. As a result, many different methods based on classical Machine Learning and Deep Learning have been proposed. Despite the success achieved by these techniques, existing methods are lacking because they provide a classification output that does not help practitioners with any information regarding the criteria that have been taken to the given classification or what information in the input data makes them arrive at their decisions. To overcome these limitations, in this paper we focus on an “explainable” method for traffic classification able to provide the practitioners with information about the classification output. More specifically, our proposed solution is based on a multi-objective evolutionary fuzzy classifier (MOEFC), which offers a good trade-off between accuracy and explainability of the generated classification models. The experimental results, obtained over two well-known publicly available data sets, namely, UniBS and UPC, demonstrate the effectiveness of our method.https://www.mdpi.com/2076-3417/11/10/4697traffic classificationfuzzy classifiermulti-objective evolutionary learning scheme |
| spellingShingle | Christian Callegari Pietro Ducange Michela Fazzolari Massimo Vecchio Explainable Internet Traffic Classification Applied Sciences traffic classification fuzzy classifier multi-objective evolutionary learning scheme |
| title | Explainable Internet Traffic Classification |
| title_full | Explainable Internet Traffic Classification |
| title_fullStr | Explainable Internet Traffic Classification |
| title_full_unstemmed | Explainable Internet Traffic Classification |
| title_short | Explainable Internet Traffic Classification |
| title_sort | explainable internet traffic classification |
| topic | traffic classification fuzzy classifier multi-objective evolutionary learning scheme |
| url | https://www.mdpi.com/2076-3417/11/10/4697 |
| work_keys_str_mv | AT christiancallegari explainableinternettrafficclassification AT pietroducange explainableinternettrafficclassification AT michelafazzolari explainableinternettrafficclassification AT massimovecchio explainableinternettrafficclassification |