# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 1 Introduction
Due to the high degree of scaling, traditional classification techniques fail [2]. A widely accepted solution to this problem is the consolidation of Software-Defined Networking (SDN) and machine learning. However, as pointed out in [4], the existing ML-based network traffic classifiers have to face various challenges, such as the randomization and masquerade in port numbers, the data encryption, the collection of data input, and the potential bias in the dataset. Another problem arises from the increasing data dimensionality and the large number of features. The determination of appropriate features (e.g., using K-PCA) or the extraction of features that may be used for classification are costly and hinder the traffic classification task. In this work, we consider GenClass [12], a low-cost mechanism classification technique in terms of memory usage and evaluation time. Since this method does not involve the training of any model but only the evolution of partition rules, it can be considered a fast method. Moreover, it can use only a small part of the original features of the problem in the classification rules, speeding up the overall process. It is not dependent on the number of features, and it is capable of revealing the hidden patterns of datasets. Furthermore, GenClass has shown impressive results in solving classification problems. Thus, we propose the use of GenClass for network traffic classification in SDN environments and evaluate its performance in the case of classification traffic flows into their original application categories. We expect the results to meet the requirements for more precise and effective administration of the network. Using a public dataset of flows from a real SDN network, we conduct experimental measurements and evaluate the ability of the method to classify traffic from six different applications. More specifically, the contribution of this work is summarized as follows:  
* We consider the classification of the network traffic generated by an SDN system, and propose the development of a classifier using the GenClass tool. * We conducted simulated experiments and evaluated the accuracy and average classification error of the proposed model. * We provide an experimental comparison between our proposed classifier and the other well-known supervised ML methods and demonstrate the outperformance of GenClass as opposed to others. The GenClass method has managed to identify the hidden associations between the feature of the used dataset and the required classes of the problem. In addition, this method can use from the initial set of features only those that contribute significantly to the decision-making to find the desired category. * We provide future directions to incorporate the Grammatical Evolution-based algorithms, such as GenClass, in the online process for incoming network traffic classification in real time. The remainder of this paper is organized as follows: in Section 2 the proposed method and the used methods are fully described, in Section 3 the experimental results are listed, and finally, in Section 4, some conclusions are presented.