A novel network traffic classification approach via discriminative feature learning
Lixin Zhao, Lijun Cai, Aimin Yu, Zhen Hui Xu, Dan Meng · 2020
Network traffic classification plays an important role in many network monitoring and security tasks. More recently, with the development of deep learning techniques, the performance of network traffic classification has been significantly improved due to the powerful feature representations learned by deep neural networks. Despite the great success that has been achieved, the problems of within-class diversity and between-class similarity are still big challenges. In this paper, we propose to train a CNN model by optimizing a new discriminative objective function, where apart from minimizing the empirical risk, a metric learning regularization term is also imposed on the learned features. This metric learning regularization term enforces the CNN model to learn more discriminative features in the mapped feature space, where the instances from the same class are closer together while the instances of different classes are farther apart. We conduct extensive experiments to evaluate the proposed method on three traffic datasets. The experimental results demonstrate that our proposed method outperforms the existing baseline methods and obtains state-of-the-art results on all the three datasets.