Internet traffic classification is vital to the areas of network operation and management. Traditionalclassification methods such as port mapping and payload analysis are becoming increasingly difficult asnewly emerged applications (e.g. Peer-to-Peer) using dynamic port numbers, masquerading techniquesand encryption to avoid detection. This paper presents a machine learning (ML) based traffic classificationscheme, which offers solutions to a variety of network activities and provides a platform of performanceevaluation for the classifiers. The impact of dataset size, feature selection, number of applicationtypes and ML algorithm selection on classification performance is analyzed and demonstrated by the followingexperiments: (1) The genetic algorithm based feature selection can dramatically reduce the costwithout diminishing classification accuracy. (2) The chosen ML algorithms can achieve high classificationaccuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexityand accuracy are both taken into account. (3) Larger dataset and fewer application types wouldresult in better classification accuracy. Finally, early detection with only several initial packets is proposedfor real-time network activity and it is proved to be feasible according to the preliminary results.
Coalition game theory is introduced to investigate the performance,fairness and stability of decorrelating group multiuser detection receiver,not only from the perspective of individual nodes,but also various coalitions and the whole system as well. Firstly,to derive how the system scale with coalition size,a stochastic model with transferable payoffs (stochastic TU-model) is provided. Secondly,to find the most preferred coalition structures from the view point of individual nodes,a model with Non-Transferable payoffs (NTU-model) is presented. Theoretical analysis and simulation results suggest that stochasticaly the grand coalition is payoff maximizing for the system as a whole,while individual nodes with good-conditioned channels may prefer local "win-win coalitions".
