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Dealing with imbalanced data is one of the main challenges in machine/deeplearning algorithms for classification. This issue is more important with logmessage data as it is typically very imbalanced and negative logs are rare. Inthis paper, a model is proposed to generate text log messages using a SeqGANnetwork. Then features are extracted using an Autoencoder and anomaly detectionis done using a GRU network. The proposed model is evaluated with twoimbalanced log data sets, namely BGL and Openstack. Results are presented whichshow that oversampling and balancing data increases the accuracy of anomalydetection and classification.

Oversampling Log Messages Using a Sequence Generative Adversarial Network for Anomaly Detection and Classification