  Insider threats are the cyber attacks from within the trusted entities of an
organization. Lack of real-world data and issue of data imbalance leave insider
threat analysis an understudied research area. To mitigate the effect of skewed
class distribution and prove the potential of multinomial classification
algorithms for insider threat detection, we propose an approach that combines
generative model with supervised learning to perform multi-class classification
using deep learning. The generative adversarial network (GAN) based insider
detection model introduces Conditional Generative Adversarial Network (CGAN) to
enrich minority class samples to provide data for multi-class anomaly
detection. The comprehensive experiments performed on the benchmark dataset
demonstrates the effectiveness of introducing GAN derived synthetic data and
the capability of multi-class anomaly detection in insider activity analysis.
Moreover, the method is compared with other existing methods against different
parameters and performance metrics.
