Mobile network traffic pattern classification with incomplete a priori information

Zhiping Jin, Zhibiao Liang, Yu Wang*, Weizhi Meng

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


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 effectively detect both known and unknown applications.

Original languageEnglish
JournalComputer Communications
Pages (from-to)262-270
Publication statusPublished - 2021


  • Constrained clustering
  • Machine learning
  • Traffic classification


Dive into the research topics of 'Mobile network traffic pattern classification with incomplete a priori information'. Together they form a unique fingerprint.

Cite this