Oversampling Log Messages using A Sequence Generative Adversarial Network for Anomaly Detection and Classification


Amir Farzad and T. Aaron Gulliver, University of Victoria, Canada


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.


Deep Learning, Oversampling, Log messages, Anomaly detection, Classification

Full Text  Volume 10, Number 5