Detection of Insider Threats Using Deep Learning

Information Technology systems are more and more susceptible to many variety of security risks, the majority of risks are mostly started by internal users. Insider threats are a complicated and difficult problem to identify and avoid because they mostly have privileged access to the network and database of company details. Businesses, organisations, and governmental organisations have a serious cybersecurity risk as a result of insider attacks. Insider threat identification is challenging because of uneven data, scant ground truth, and potentially changing user behavior. This work provides an insider threat detection method based on anomaly detection using unsupervised learning. Computer network and system security are severely hampered by insider threats. Theft of intellectual property, sabotage, the release of sensitive data, and web application attacks are examples of malevolent actions committed by authorised users that have the potential to do serious harm. It is the responsibility of the organisation to protect all network layers and guard against intrusions. Using historical data, the extracted behavioral traits using the Deep Learning method. The project made advantage of the publicly accessible Computer Emergency Response Team (CERT) insider threats dataset.