# Explainable Internet Traffic Classification
###### Abstract
[[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal:  
**Citation: [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON], [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation: [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation: [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Explainable Internet Traffic Classification. _Appl. Sci._**2021**, _11_, 4697. [[https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697)]([https://doi.org/10.3390/app11104697](https://doi.org/10.3390/app11104697))  
Footnote †: journal: Applied
Among these, Support Vector Machine (SVM) [4] and deep learning techniques [5] have emerged as powerful tools for traffic classification and other network application, such as intrusion detection [6] and other cyber security application [7; 8]. Indeed, these techniques, and especially SVM, represent an almost _de facto_ standard in the field. Such methods are able to provide very high accuracy values, often just observing a few traffic statistics computed over the first packets of each flow. Nonetheless, all of the methods based on machine learning algorithms present a common drawback, as the generated models are seen as black boxes characterized by a low \"explainability\" level. Indeed, the classification result does not provide the practitioners with any information regarding the criteria that have taken to the given classification, or what information in the input data makes them arrive at their decisions. This is usually justified by the fact that the main goal traditionally pursued is to make the model matching reality (i.e., accurate models), without actually caring for explainable models.