“Machine Learning for Cybersecurity: Standing on the Shoulders of Giants”
Thursday, Oct. 24, 1:00 pm
310 Larsen Hall
In an era characterized by increasing cybersecurity threats, we have witnessed the ever-continuing competition between system designers/ manufacturers and adversaries that maliciously break the security of systems. This is partially due to the lack of systematic and provable methods, which can assess the security of a system. This lack of methods is present despite the existence of well-known, and acknowledged frameworks developed in cryptography, and its “sister field,” i.e., machine learning. This talk aims to explore the close relationship between machine learning and cryptography and will provide examples of physical systems, whose security can be assessed from the point of view of machine learning.
Dr. Fatemeh Ganji is a postdoctoral fellow at the Florida Institute for Cybersecurity (FICS) Research. She received her Ph.D. degree in Electrical Engineering from the Technical University of Berlin in 2017. She has focused her research activities on the applied and theoretical machine learning techniques for the security assessment of hardware primitives. For her work on the learnability of Physically Unclonable Functions (PUFs), she has received BIMoS PhD Award in 2018. Before joining FICS, Dr. Ganji was a postdoctoral research fellow at T-Labs, Telekom Innovation Laboratories and the Technical University of Berlin.