The accuracy obtained for SVM is 92.3%, NB is 96.79% and the nearest centroid is 91.02%. The network traffic traces are captured and flows features are generated, which is sent to the classifier for prediction. Three different supervised learning models, namely Support Vector Machine (SVM), nearest centroid and Naïve Bayes (NB), are applied to classify the data traffic based on the applications in a software-defined network platform. To overcome this drawback, the integration of Software Defined Network (SDN) architecture and machine learning technology is proposed in this paper. The different primitive techniques of network traffic classification have failed to provide reliable accuracy because of 1000 fold scaling in the amount of devices as well as flows. Network Traffic Classification is significant in recent days due to rapid growth in the number of internet consumers. It does not store any personal data.Traffic classification with accuracy is of prime importance in network activities such as security monitoring, traffic engineering, fault detection, accounting of network usage, billing and for providing differentiation in Quality of Service (QoS) parameters of the various network services. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly.
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