Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

► In this study, a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer traffic is proposed. ► We first compute the minimum classification error (MCE) matrix using genetic algorithm. ► The MCE matrix is then used during the preprocessing step to map the original dataset into a new space. ► The mapped data set is then fed to different classifiers. ► The experimental results demonstrate that the proposed mapping scheme achieves, on average, 8% higher accuracy in classification of the P2P traffic compare to other solutions.