New project to help identify and predict insider threats

Jingrui He
Jingrui He, Associate Professor

Insider threats are one of the top security concerns facing large organizations. Current and former employees, business partners, contractors—anyone with the right level of access to a company’s data—can pose a threat. The incidence of insider threats has increased in recent years, at a significant cost to companies. Associate Professor Jingrui He is addressing this problem in a new project that seeks to detect and predict insider threats. She has been awarded a three-year, $200,000 grant from the Digital Transformation Institute for her project, "Multi-Facet Rare Event Modeling of Adaptive Insider Threats."

According to He, the question her team seeks to answer is, "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?"

Insider threats are typically rare and involve only a small percentage of employees. In order to evade current detection systems, adaptive insiders will change their strategies when carrying out the attacks.

"Initially, we will integrate the information from multimodal data to detect both outliers and rare category types of insider threats," He said. "Then we will study the adaptive behaviors of insider threats and propose dynamic update techniques based on the models we develop."

He's team will work closely with Development Operations staff at the Digital Transformation Institute, a research consortium jointly hosted by the University of Illinois and University of California, Berkeley. After implementing the models on the platform, the team will use various public data sets, including the Computer Emergency Response Team (CERT) Insider Threat data set, to evaluate the models. John R. Birge, Hobart W. Williams Distinguished Service Professor of Operations Management at The University of Chicago Booth School of Business, will serve as co-principal investigator on the project.

He's general research theme is to design, build, and test a suite of automated and semi-automated methods to explore, understand, characterize, and predict real-world data by means of statistical machine learning. She received her PhD in machine learning from Carnegie Mellon University.

Updated on
Backto the news archive

Related News

Hoang defends dissertation

Doctoral candidate Linh Hoang successfully defended her dissertation, "Natural Language Processing to Support Evidence Quality Assessment of Biomedical Literature," on December 8.

Linh Hoang

Wang research group receives ASONAM Best Paper Award

A paper coauthored by PhD student Lanyu Shang and members of Associate Professor Dong Wang's research group, the Social Sensing and Intelligence Lab, received the best paper award in the research track during the 2022 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2022).

Dong Wang

He research group presents at NeurIPS

Members of Associate Professor Jingrui He's research group, the iSAIL Lab, will present their research at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022), which will be held from November 29-December 1 in New Orleans, Louisiana, and also virtually. NeurIPS is one of the most prestigious and competitive international conferences in machine learning and computational neuroscience.  

Jingrui He

Schiller authors new book on the development of U.S. telecommunications

Professor Emeritus Dan Schiller has authored a new book on the progression of telecommunications systems in the United States. In Crossed Wires: The Conflicted History of U.S. Telecommunications from the Post Office to the Internet, which will be released by Oxford University Press in February 2023, Schiller draws on archival documents to argue that it was not technology but political economy that drove the evolution of the telecommunications industry.

Dan Schiller