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Insider attacks are one of the most challenging cybersecurity issues forcompanies, businesses and critical infrastructures. Despite the implementedperimeter defences, the risk of this kind of attack is still very high. Infact, the detection of insider attacks is a very complicated security task andpresents a serious challenge to the research community. In this paper, we aimto address this issue by using deep learning algorithms Autoencoder andVariational Autoencoder deep. We will especially investigate the usefulness ofapplying these algorithms to automatically defend against potential internalthreats, without human intervention. The effectiveness of these two models isevaluated on the public dataset CERT dataset (CERT r4.2). This version of theCERT Insider Threat Test dataset includes both benign and malicious activitiesgenerated from 1000 simulated users. The comparison results with other modelsshow that the Variational Autoencoder neural network provides the best overallperformance with a greater detection accuracy and a reasonable false positiverate

Insider Detection using Deep Autoencoder and Variational Autoencoder Neural Networks