# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 1 Introduction  
SDN constitutes a new pathway to implement networks, to address technical issues (routing, traffic engineering, load balance, security, etc. ), and provide automation and programming capabilities to the whole network. SDN promises a new era for next-generation networks that dictates a top-down approach to network design. The flexibility and ease of management of networks introduced by SDN are due to (a) the separation of the control plane from the data forwarding plane; (b) the integration of control, management, and configuration; (c) the hallmarks openness and interoperability of SDN architecture that promise vendor-neutral network implementations. A vital element of the SDN framework is the OpenFlow protocol, which is used as the standard protocol to describe the communication between the two distinct planes: the control plane and the data plane [1]. The evolution of these networks introduces the P4 (Protocol-Independent Packet Processors) as a new forwarding model, that flexibly implements the programmer's decisions about how the packets and data flows will be processed. However, the traffic on contemporary networks is growing exponentially, and the introduction of simple rules (OpenFlow or P4) is not enough to optimally manage data traffic. One solution is to embed intelligence into physical or virtual network devices tofacilitate data traffic management [2]. More specifically, it requires the implementation of methods for automated categorization of data traffic according to its features, or network traffic classification. The term Network Traffic Classification refers to the process of identifying and associating traffic flows or data packets with the causal source (e.g., application, protocol, network services) or distinguishing them into categories based on the size of flows or priorities related to the needs of each application. Classification aims at allocating packets or flows into different and distinct categories based on an automated matching process [3; 4]. Network traffic classification of data is widely used to more effectively manage computer networks, monitor their operations, estimate the Quality of Service, make network design decisions, predict future traffic patterns, provide dynamic access control, and improve service quality. It has a significant role in the efficient execution of basic network functions such as routing, traffic prediction, resource allocation, traffic engineering, load balancing, effective management functions, malicious flow identification, network traffic forecasting, and flow control [3; 4; 5; 6; 7; 8; 9]. The adoption of an appropriate classification strategy by ISPs allows them to implement efficient charging models for their customers in proportion to the resources and categories of applications they wish to use. Yet, this makes it easier for ISPs to support the Quality of Services and lawful intercept capabilities [10; 11]. According to [PERSON] et al. [6], traffic classification contributes to better network management and more efficient resource allocation in the following cases: (a) in discriminating elephant flow for implementing the best scheduling policy in data centers; (b) in matching flows with their applications (application-aware traffic classification) for optimal network management; and (c) in determining the QoS levels of traffic flows to optimize resource allocation policies. Consequently, traffic classification is a highly important task for SDN. The accurate identification and classification of network traffic is essential to ensuring efficiency, stability, security, seamless operation, and optimal network management. Effective monitoring and management of network data traffic requires the use of efficient and highly accurate classification techniques in an effort to improve network performance. These requirements are necessitated by (a) the huge volume of web traffic; and (b) the large number of new emerging applications that claim a significant share of traffic.