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Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks
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DOI：10.1109/TVT.2020.2995160.png)
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Abstract
En 中文
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.
Keywords:
Quality of experience
Anomaly detection
Cellular networks
Data preprocessing
Machine learning
Support vector machines
Knowledge engineering
Machine learning
quality of experience
key quality index
one-class support vector machine
anomaly detection
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