Machine Learning in Profiling Side-Channel Analysis | Seminar

by Fahrettin Ay | Jan 04, 2022
Stjepan Picek will give a seminar on Wednesday, January 5, 2022 at 15:30 via Zoom.
An online seminar titled Machine Learning in Profiling Side-Channel Analysis will be given via Zoom on Wednesday, January 5, 2022 at 15:30 by Assoc. Prof. Dr. Stjepan Picek. Anyone interested in the seminar is invited to attend.

Abstract:
In side-channel analysis (SCA), the attacker exploits weaknesses in the physical implementations of cryptographic algorithms. In the last decade, profiling side-channel attacks based on machine learning proved to be very successful in breaking cryptographic implementations in various settings. Still, despite successful attacks, even in the presence of countermeasures, there are many open questions. In this talk, we first discuss the best success stories on machine learning-based side-channel analysis. Afterward, we concentrate on critical open questions and research directions that still need to explore. Finally, we connect the machine learning progress in profiled SCA with developments in other security domains.

Stjepan Picek is an associate professor at Radboud University, The Netherlands. His research interests are security/cryptography, machine learning, and evolutionary computation. Prior to the associate professor position, Stjepan was an assistant professor at TU Delft, and a postdoctoral researcher at MIT, USA and KU Leuven, Belgium. Stjepan finished his PhD in 2015 with a topic on cryptology and evolutionary computation techniques. Stjepan also has several years of experience working in industry and government. Up to now, Stjepan has given more than 20 invited talks at conferences and summer schools and published more than 100 refereed papers. He is a general co-chair for Eurocrypt 2021, program committee member and reviewer for a number of conferences and journals, and a member of several professional societies.


Zoom Link:

https://itu-edu-tr.zoom.us/j/98446224783?pwd=OEpXWDNqbjRqQkF5eExYMUQ1Yk9BQT09

Meeting ID: 984 4622 4783
Passcode: 341560

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