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