# Explainable Internet Traffic Classification
###### Abstract
Nowadays, several new requirements have emerged related to fairness or unbiasedness, privacy, reliability, robustness, causality, and/or trust posing the need of deploying systems that must provide explanations for the taken decisions, where necessary [9]. Therefore, traffic classifiers, as well as other traffic analysis systems, must be optimized not only for accuracy but also for the other criteria previously listed. In such a context, a lot of research efforts are nowadays focusing on \"explainable\" methods (e.g., explainable artificial intelligence), where explainability \"encompasses ML/AI systems for opening black box models, for improving the understanding of what the models have learned and/or for explaining individual predictions\" [9]. In this specific area, a recent work in [10] clearly indicates that exploiting the synergy between Fuzzy Rule-Based Systems (FRBSs) and Evolutionary Algorithms is one of the most straightforward ways of combining accuracy and interpretability/explainability in machine learning-based tools. For such a reason, in this work, which significantly extends the preliminary results presented in [11], we propose a traffic classification approach based on multi-objective evolutionary fuzzy classifiers (MOECs) [12; 13]. Specifically, MOECs deal with the application of Multi-Objective Evolutionary Algorithms (MOEAs) [14] for generating a collection of Fuzzy Rule-Based Classifiers (FRBCs) characterized by different trade-offs between their accuracy and their explainability level [15]. We recall that FRBCs adopt (i) a rule base composed of _linguistic_ IF-THEN rules and (ii) a database which contains the description of the linguistic terms adopted for the fuzzy discretization of each input variable. A specific inference mechanism is adopted for taking a decision whenever a new input is presented to the system. In this contribution, we exploit the PAES-RCS algorithm, in which the accuracy is calculated in terms of percentage of correctly classified flows of internet traffic. As regards the explainability level, it is calculated in terms of total rule length (TRL), namely, the total number of conditions taken into consideration in the whole rule base. Low values of TRL are associated with rule bases which contain a reduced number of simple rules (i.e., rules in which a low number of conditions are adopted in their antecedent). Note that PAES-RCS has been successfully exploited in a number of recent contributions on real-world applications [15; 16]. To evaluate and validate the proposed approach, we have used two publicly available data sets, namely, UniBS and UPC, showing that our system can achieve nearly optimal performance, while simultaneously guaranteeing the explainability of the classification results. We also compared the results achieved by MOECs with the ones achieved by two classical ML-based classification algorithms, namely, SVM and Decision Trees. SVM algorithms have been chosen as they represent the _de facto_ standard among machine learning algorithms commonly adopted for solving the internet traffic classification problem. However, SVM models are characterized by a very low explainability level. Regarding decision trees, as from the trees it is possible to extract a set of decision rules, they represent a category of interpretable models among classical machine learning classifiers. However, their rules are not linguistic and the final models are often described by a large number of parameters, namely, the number of nodes and leaves. Thus, also the explainability level of decision trees is often compromised. As a counterpart, the proposed approach, based on MOEFCs, generates models characterized by good trade-offs between their accuracy and their explainability. The remainder of the paper is organized as follows. In Section 2, we discuss some notable related works, while in Section 3.1 we describe the used data sets. Then, in Section 3.2 we introduce the experimented explainable traffic classification approach. The achieved results are shown in Section 4.