Mobile traffic classification is a topic of interest for researchers focused on improving the network capacity or for those seeking to identify potential risks to users’ privacy. In recent years, traffic classification accuracy has significantly improved thanks to machine learning techniques. These techniques allow traffic identification even if it is encrypted, as in mobile networks. In this paper, we show that it is feasible to classify mobile traffic applications with high accuracy using downlink control information (DCI) messages and machine learning. The DCI messages are collected using a sniffer located near the base station. Then we extract the statistical features of the bursts and inter-burst periods of the traffic generated by mobile applications at the physical layer. This strategy uses few features and does not require a big dataset. We have tested our approach on a 4G cellular network testbed and a commercial 4G cellular network. The results show an accuracy greater than 92% and 95% for application and category classification, respectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Mobile traffic classification through burst traffic statistical features


    Contributors:


    Publication date :

    2023-06-01


    Size :

    1302352 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Flow-based traffic retrieval using statistical features

    Zhang, Jun / Goscinski, Andrzej | IEEE | 2016


    CENTIME: A Direct Comprehensive Traffic Features Extraction for Encrypted Traffic Classification

    Maonan, Wang / Kangfeng, Zheng / Ning, Xinyi et al. | IEEE | 2021


    Traffic classification on mobile core network considering regularity of background traffic

    Suzuki, Masaki / Watari, Masafumi / Ano, Shigehiro et al. | IEEE | 2015


    Traffic flow data compression considering burst components

    Feng, Shuo / Ke, Ruimin / Wang, Xingmin et al. | IET | 2017

    Free access

    Traffic flow data compression considering burst components

    Feng, Shuo / Ke, Ruimin / Wang, Xingmin et al. | Wiley | 2017

    Free access