Semi-supervised and Compound Classification of Network Traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labeling unlabeled flows according to their correlation to the pre-labeled flows. Consequently, the traffic classifier has better performance due to the extended size and quality of the supervised data sets. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method outperforms the state-of-the-art flow statistical feature based classification methods.