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

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posted on 22.01.2020 by Yutao Lu, Juan Wang, Miao Liu, Kaixuan Zhang, Guan Gui, Tomoaki Ohtsuki, Fumiyuki Adachi
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.

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Email Address of Submitting Author

guiguan@njupt.edu.cn

Submitting Author's Institution

Nanjing University of Posts and Telecommunications

Submitting Author's Country

China

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in IEEE Transactions on Vehicular Technology

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