  Generative adversarial networks have been able to generate striking results
in various domains. This generation capability can be general while the
networks gain deep understanding regarding the data distribution. In many
domains, this data distribution consists of anomalies and normal data, with the
anomalies commonly occurring relatively less, creating datasets that are
imbalanced. The capabilities that generative adversarial networks offer can be
leveraged to examine these anomalies and help alleviate the challenge that
imbalanced datasets propose via creating synthetic anomalies. This anomaly
generation can be specifically beneficial in domains that have costly data
creation processes as well as inherently imbalanced datasets. One of the
domains that fits this description is the host-based intrusion detection
domain. In this work, ADFA-LD dataset is chosen as the dataset of interest
containing system calls of small foot-print next generation attacks. The data
is first converted into images, and then a Cycle-GAN is used to create images
of anomalous data from images of normal data. The generated data is combined
with the original dataset and is used to train a model to detect anomalies. By
doing so, it is shown that the classification results are improved, with the
AUC rising from 0.55 to 0.71, and the anomaly detection rate rising from 17.07%
to 80.49%. The results are also compared to SMOTE, showing the potential
presented by generative adversarial networks in anomaly generation.
