"New Project to Help Identify and Predict Insider Threats"
Insider attacks remain one of the most serious security issues faced by large businesses. Anyone accessing a company's data can pose a threat, including current and former employees, business partners, and contractors. Insider threats have become more common in recent years, at a high cost to businesses. Jingrui He, Associate Professor at the University of Illinois Urbana-Champaign, is tackling this issue with a new project that aims to detect and predict insider threats. The C3.ai Digital Transformation Institute has awarded her a three-year, $200,000 grant for her project, "Multi-Facet Rare Event Modeling of Adaptive Insider Threats." According to He, her team's goal is to answer the question, "How can we detect and model the rare and adaptive insider threats in big organizations based on multimodal data, such as computer logon and logoff activities, email exchanges, and web browsing history?" The team will integrate the information from multimodal data to detect outliers and rare category types of insider threats. Then they will study the adaptive behaviors of insider threats and propose dynamic update techniques based on the models they develop. This article continues to discuss the new project on the identification and prediction of insider threats.