NowadaysInternet does not provide an exchange of information between applications and networks, which may resultsin poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. SDN is an innovative and programmable networking architecture, representing the direction of the future network evolution. Accurate traffic classification over SDN is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service(QoS)to providing operators with useful forecasts for long-term provisioning. In this paper,four variants of Neural Network estimatorare usedto categorize traffic by application.The proposed methodisevaluated in the four scenarios:feedforward;Multilayer Perceptron (MLP);NARX (Levenberg-Marquardt) and NARX (Naïve Bayes). These scenarios respectively provide accuracy of 95.6%,97%, 97% and 97.6%.

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