WekaTIE, a traffic classification plugin integrating TIE and Weka

The exploding volume of network traffic and expanding Quality of Service (QoS) requirements from emerging multimedia and interactive applications in the last decade demand improved internet traffic engineering techniques. In particular, traffic classification and packet marking became essential components for end-to-end QoS assurance of different traffic classes. In this paper we present WekaTIE, a TIE (traffic Identification Engine) plugin for network traffic classification, which integrates TIE and Weka (Waikato Environment for Knowledge Analysis), enabling the construction of classification methodologies based on machine learning techniques on the fly. Weka implements many well known machine learning algorithms such as bayesian methods, naïve bayes, bayesian networks, and decision tree learners, among others. This tool is integrated in our proposal to generate a model which is directly used by WekaTIE plugin for internet traffic classification using machine learning methodologies. The plugin architecture is described, including an example of its usage, and a preliminary evaluation is presented.