The dissertation investigated the creation of an anomaly detection approach to
identify anomalies in the SGW elements of a LTE network. Unsupervised techniques
were compared and used to identify and remove anomalies in the training data set.
This “cleaned” data set was then used to train an autoencoder in an semi-supervised
approach. The resultant autoencoder was able to indentify normal observations. A
subsequent data set was then analysed by the autoencoder. The resultant
reconstruction errors were then compared to the ground truth events to investigate
the effectiveness of the autoencoder’s anomaly detection capability
