# Explainable Internet Traffic Classification
## 2 Related Work  
Research on traffic classification has been quite prolific in the years and, as a consequence, many works have been written on the topic. Therefore, the aim of this section is not to provide the reader with a comprehensive review of the related works (for which we refer the reader to the surveys on the topic), but just to point out some works significant for our specific proposal.  
Machine Learning techniques have been first applied to network traffic classification in 1994 [17] and since then many different methods have been proposed, as detailed in some recent surveys [3; 18].  
Among the many proposals, particular interest has been raised by classifiers based on Support Vector Machine (SVM). One of the first significant work on the application of SVM to traffic classification is [19], where the authors apply one of the approaches to solving multi-class problems with SVMs and describe a simple optimization algorithm that allows the classifier to perform correctly with as little training as a few hundred samples. Since then, many other works have proposed SVM-based methods [4; 20; 21; 22; 23] and, as a result, SVM is nowadays considered as a _de facto_ standard in the field. Nonetheless, as already discussed, all of these works propose methods based on black-box models that do not provide any information about the classification criteria.  
As far as Fuzzy Rule-Based Classifiers (FRBCs) are concerned, given their ability to deal with vague and noisy data and to explain how the classification task is performed, they have been widely exploited in several contexts, such as medical diagnosis applications [24], industrial applications [25], and Internet of Things [26]. In the years, several techniques to generate and optimize the structure of FRBCs have been proposed, often without taking into consideration how this maximization affects the FRBC explainability, but only in the last decade, researchers have also focused their attention on the explainability aspects of FRBCs [27]. As accuracy and explainability are conflicting objectives, the generation of the FRBS structure has been modeled as a multi-objective optimization problem. Multi-objective evolutionary algorithms (MOEAs) have been successfully employed to tackle this optimization problem and the term multi-objective evolutionary fuzzy systems (MOEFSs) has been coined [12; 28] to identify FRBSs generated by MOEAs. Since then, many papers have proposed the use of MOEFSs in classification problems [16; 29; 30; 31; 32; 33].  
In the specific context of traffic classification, there are some works [34; 35] that propose the use of fuzzy models. The work in [34] discusses the application of hybrid models in which fuzzy theory elements are included into a neural network architecture. As regards the contribution discussed in [35], the authors propose an approach which combines a decision trees and fuzzy membership functions for dealing with noisy and vague data. Note that both works include in their experimental analysis a comparison with the traffic classification methods based on SVM. Nonetheless, to the best of our knowledge, our work is the first to propose and evaluate in a systematic way, the application of MOEFCs to generate explainable models for network traffic classification.