Insider threat prediction is the security risk prediction that aims to identify an internal threat emerging from inside the network of an organization. Insider threats cause high damage to the organization than external attacks owing to their complexity, secrecy, and infrequency of malicious internal actions. Traditional methods for the prediction of insider threats are time-consuming and inability to handle complex, nonlinear, and non-stationary data.
Deep learning models effectively manipulate large heterogeneous sources of structured and unstructured data. Deep learning models proactively predict insider cyber attacks by utilizing deep neural networks. Deep neural networks automatically extract the powerful features to predict insider attacks. The deep learning model predicts the insider threats by analyzing the attacker-s abnormal behavior and extracting the discriminatory features. Deep neural networks provide accurate and high performance for proactive prediction of insider threats.