  Dealing with imbalanced data is one of the main challenges in machine/deep
learning algorithms for classification. This issue is more important with log
message data as it is typically very imbalanced and negative logs are rare. In
this paper, a model is proposed to generate text log messages using a SeqGAN
network. Then features are extracted using an Autoencoder and anomaly detection
is done using a GRU network. The proposed model is evaluated with two
imbalanced log data sets, namely BGL and Openstack. Results are presented which
show that oversampling and balancing data increases the accuracy of anomaly
detection and classification.
