General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles …

Project-based learning continues to inspire cybersecurity students: the 2018--2019 SFS research studies at UMBC

Citation: Enis Golaszewski, Alan T. Sherman, Linda Oliva, Peter A. H. Peterson, Michael R. Bailey, Scott Bohon, Cyrus Bonyadi, Casey Borror, Ryan Coleman, Johannah Flenner, Elias Enamorado, Maksim E. Eren, Mohammad Khan, Emmanuel Larbi, Kyle Marshall, William Morgan, Lauren Mundy, Gabriel Onana, Selma Gomez Orr, Lauren Parker, Caleb Pinkney, Mykah Rather, Jimmy Rodriguez, Bryan Solis, Wubnyonga Tete, Tsigereda B.