In complex networks systems like mobile edge infrastructures, real-time traffic classification according to application types is an enabling technique for network resource optimization and advanced security management. State-of-the-art schemes take advantage of machine learning techniques to train classification models based on behavioral characteristics of network traffic flows. Nonetheless, most existing studies assume complete a priori information of the application classes and formulate the task as a standalone multi-class classification problem. Such classification models cannot properly handle the unknown applications that are absent from the training set during the time of training. In this work, we propose a practical mobile network traffic classification scheme that builds robust classifiers based on incomplete a priori information. Specifically, the core idea is to extract the unknown patterns emerging in the network periodically to complement the initial labeled data set that only consists of a limited number of known applications. We propose two algorithms for the unknown pattern extraction step. One is based on iterative asymmetric binary classification and the other is based on constrained clustering. Empirical results based on a public data set show that the proposed scheme can effetively detect both known and unknown applications.

在诸如移动边缘基础设施之类的复杂网络系统中，根据应用程序类型进行实时流量分类是一种用于网络资源优化和高级安全管理的支持技术。最新的方案利用机器学习技术来根据网络流量的行为特征来训练分类模型。但是，大多数现有研究都假定完整地了解了应用程序类别的先验信息，并将该任务表述为一个独立的多类分类问题。这种分类模型无法正确处理训练期间训练集中缺少的未知应用程序。在这项工作中，我们提出了一种实用的移动网络流量分类方案，该方案基于不完整的先验信息构建鲁棒的分类器。具体来说，核心思想是定期提取网络中出现的未知模式，以补充仅由有限数量的已知应用程序组成的初始标记数据集。对于未知模式提取步骤，我们提出了两种算法。一种基于迭代不对称二进制分类，另一种基于约束聚类。基于公共数据集的经验结果表明，该方案可以有效地检测已知和未知应用程序。一种基于迭代不对称二进制分类，另一种基于约束聚类。基于公共数据集的经验结果表明，该方案可以有效地检测已知和未知应用程序。一种基于迭代不对称二进制分类，另一种基于约束聚类。基于公共数据集的经验结果表明，该方案可以有效地检测已知和未知应用程序。