  Insider attacks are one of the most challenging cybersecurity issues for
companies, businesses and critical infrastructures. Despite the implemented
perimeter defences, the risk of this kind of attack is still very high. In
fact, the detection of insider attacks is a very complicated security task and
presents a serious challenge to the research community. In this paper, we aim
to address this issue by using deep learning algorithms Autoencoder and
Variational Autoencoder deep. We will especially investigate the usefulness of
applying these algorithms to automatically defend against potential internal
threats, without human intervention. The effectiveness of these two models is
evaluated on the public dataset CERT dataset (CERT r4.2). This version of the
CERT Insider Threat Test dataset includes both benign and malicious activities
generated from 1000 simulated users. The comparison results with other models
show that the Variational Autoencoder neural network provides the best overall
performance with a greater detection accuracy and a reasonable false positive
rate
