Abstract: Traffic classification is an automatic method that categorizes electronic network traffic per varied parameters into variety of traffic categories. Many supervised classification algorithms and unsupervised clump algorithms have been applied to reason web traffic. Traditional traffic classification strategies embody the port-based prediction strategies and payload-based deep scrutiny strategies. In current network environment, the traditional strategies suffer from variety of sensible issues, such as dynamic ports and encrypted applications. In order to enhance the classification accuracy, Support Vector Machine (SVM) and Na´ve Bayes estimator is projected to reason the traffic by application. In this, traffic flows are represented mistreatment the discretized applied math options and flow correlation data is modelled by bag-of-flow (BoF). This methodology uses flow statistical feature based mostly traffic classification to boost feature discretization. This approach for traffic classification improves the classification performance effectively by incorporating correlated data into the classification method. The experimental results show that the proposed theme will come through far better classification performance than existing progressive traffic classification strategies.
Keywords: Support Vector Machine (SVM), Traffic Classification, Supervised algorithm, Na´ve Bayes.