Moeti Masine is a research scientist at Norfolk State University. His research focus is on in-person and crowd studies and perception modelling using machine learning. He is researching the effects of the Human-In-The-Loop in cybersecurity. This involves quantifying human insight levels and building predictive machine learning models for the effects of cybersecurity applications on humans and vice-versa. In cyber security providing protection against or detecting advanced persistent threats (APT) involves the obvious technical aspect. However, humans are highly involved on both sides of the APT divide. Humans are building malicious AI models and they are also building benevolent ones while also making decisions when reviewing the results of these models. His research seeks to strengthen the partnership between humans and machines to build stronger cybersecurity AI models through the modelling of the Human-In-The-Loop in cybersecurity. Using machine learning, Masine is answering questions involving how system and environment variables impact decisions that humans make in a cybersecurity context with the intention of predicting the effectiveness of cybersecurity techniques under given conditions.
He previously worked at Google, Adobe Systems, and the US Army Research Lab.
Masine received a B.S. in applied computer acience from the University of the District of Columbia in 2005, M.S. in computer science from Norfolk State University in 2010, and Ph.D. in computer science from Virginia Tech in 2020.