Automated Email Generation for Targeted Attacks

The advancement of generative language models has been a force for good, but also for evil. On the show, Avisha Das, a post-doctoral scholar at the University of Texas Health Center, joins us to discuss how attackers use machine learning to create unsuspecting phishing emails. She also discussed how she used RNN for automated email generation, with the goal of defeating statistical detectors.

Guest

Avisha Das: Avisha Das is a Postdoctoral Scholar at the School of Biomedical Informatics, University of Texas Health Science Center in Houston. Her research focuses on developing automated natural language understanding and generation models, targeted towards short text generation or task-based conversation. She received her Ph.D. from University of Houston in 2020, where her thesis focused on the feasibility of generating automated social engineering attacks at scale and the likelihood of humans falling victim to such threats. She is currently a BIG-TCR CPRIT Postdoctoral Fellow at UTHealth and working on mining biomedical knowledge for automated content distillation in cancer research. Her research interests include Biomedical Knowledge Mining, Natural Language Generation, and Security Analytics.