# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 4 Conclusions  
This work focuses on traffic classification in an SDN environment. Traffic classification is an essential tool for network management, Quality of Service (QoS), security, network optimization, and cost reduction. Thus, we strive to find an efficient solution for SDN traffic classification. The separation of the control plane and the data plane in SDN facilitates the integration of classification tools either in the control plane or the data plane. We propose the use of the GenClass tool, for which, using the SDN-traffic.csv dataset, we find very high accuracy in traffic data classification in the application classification.  
In reality, networks are more complex, and data traffic patterns are influenced by user sharing, scale, the advent of new protocols, application intelligence, and network stability [10]. The performance of the proposed classifier creates new opportunities for handling such situations and motivates further investigation. For example, for future work, we will explore the integration of GenClass into a controller and its real-time operation. Moreover, we will investigate the applicability of this classifier in the infrastructure layer of SDN using the P4 language.  
As it is clear from past studies and as pointed out by several researchers, a key problem is the collection of the data to be used as inputs to the traffic classification models. The collection of data requires systematic monitoring of the network. Such a process can be costly, introduce overhead, and also determine the quality and accuracy of the results. An emerging approach for network monitoring [48; 49] that is applicable to SDN networks and provides solutions to the above problems is network tomography [50]. Given the increasing scale and complexity of modern networks, the use of network tomography to  
Figure 2: A schematic representation of the execution time for each machine learning method. The vertical axis represents average execution time for each method measured in seconds.  
obtain flow characteristics presents significant advantages such as low overhead, directness, and improvement of network traffic. Thus, future work could be a thorough investigation of the coexistence of network tomography and network traffic classification in SDN systems.  
Moreover, we will investigate the applicability of this classifier in the infrastructure layer of SDN using the P4 language.  
[PERSON] and [PERSON] conceived the idea and methodology and supervised the technical part regarding the software. [PERSON] conducted the experiments, employing several datasets, and provided the comparative experiments. [PERSON] and [PERSON] performed the statistical analysis and all other authors prepared the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research has been financed by the European Union: Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan under the call RESEARCH--CREATE--INNOVATE, project name \"iCREW: Intelligent small craft simulator for advanced crew training using Virtual Reality techniques\" (project code:TAEDK-06195).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.