Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

Yutao Lu, Juan Wang, Miao Liu, Kaixuan Zhang, Guan Gui, Tomoaki Ohtsuki, Fumiyuki Adachi

    Research output: Contribution to journalArticlepeer-review

    11 Citations (Scopus)


    The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-Tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

    Original languageEnglish
    Article number9096623
    Pages (from-to)8459-8467
    Number of pages9
    JournalIEEE Transactions on Vehicular Technology
    Issue number8
    Publication statusPublished - 2020 Aug


    • Machine learning
    • anomaly detection
    • key quality index
    • one-class support vector machine
    • quality of experience

    ASJC Scopus subject areas

    • Automotive Engineering
    • Aerospace Engineering
    • Electrical and Electronic Engineering
    • Applied Mathematics


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