Research Information System
24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Verona, Italy, 16 September 2020, vol.176, pp.185-195
Anomaly detection is considered as a challenging task due to its
imbalanced and unlabelled nature. To overcome this challenge, the
combination of different machine learning approaches such as supervised,
unsupervised, semi-supervised learning are proposed in the literature.
With the advent of neural networks and generative models, different
methodologies derived from neural networks are applied to anomaly
detection tasks. In this study, we use the KDDCUP99 data set, consider
it as an anomaly detection task, and implement BiGAN, considering it as a
one-class anomaly detection algorithm. Since generator and
discriminator are highly dependent on each other in the training phase,
to reduce this dependency, in this paper, we propose two different
training approaches for BiGAN by adding extra training steps to it. We
also demonstrate that proposed approaches increased the performance of
BiGAN on anomaly detection task.