Insider Threat Detection Using Adversarial Learning and Deep Learning

This research focused on performing insider threat detection using deep learning models. In addition, this thesis also studied the robustness aspects of the designed models in the presence of adversarial data samples during the training and testing phases. The results demonstrated that the machine learning models trained with adversarial data samples are resistant to adversarial attacks.