  This article presents an automatic malfunction detection framework based on
data mining approach to analysis of network event sequences. The considered
environment is Long Term Evolution (LTE) for Universal Mobile Telecommunication
System (UMTS) with sleeping cell caused by random access channel failure.
Sleeping cell problem means unavailability of network service without triggered
alarm. The proposed detection framework uses N-gram analysis for identification
of abnormal behavior in sequences of network events. These events are collected
with Minimization of Drive Tests (MDT) functionality standardized in LTE.
Further processing applies dimensionality reduction, anomaly detection with
k-nearest neighbor, cross-validation, post-processing techniques and efficiency
evaluation. Different anomaly detection approaches proposed in this paper are
compared against each other with both classic data mining metrics, such as
F-score and receiver operating characteristic curves, and a newly proposed
heuristic approach. Achieved results demonstrate that the suggested method can
be used in modern performance monitoring systems for reliable, timely and
automatic detection of random access channel sleeping cells.
