Abstract:
Adopting an accurate anomaly detection mechanism
is crucial for industrial software systems in order to prevent
system outages that can deteriorate system availability. However,
employing a supervised machine learning technique to detect
anomalies in large production scale industrial software systems
is highly impractical due to the requirement of annotated data.
This raises the need for comprehensive semi-supervised and
unsupervised anomaly detection mechanisms. This paper presents
the application of Generative Adversarial Network (GAN) based
models to detect system anomalies using semi-supervised oneclass
learning. We show that the use of a variant of GAN known
as bidirectional GAN (BiGAN) gives augmented results when
compared to the traditional GAN based anomaly detection, for
the selected industrial system. Moreover, the experiments clearly
show that the performance of the BiGAN has a direct correlation
with the dimensions of the dataset used for training. The BiGAN
even tends to outperform the well-established semi-supervised
One-class SVM classifier and a prominent generative network
for semi-supervised anomaly detection, Variational Autoencoders
(VAEs) when the size of the feature space increases.
