In side-channel analysis (SCA), the attacker exploits weaknesses in the physical implementations of cryptographic algorithms.
In the last decade, profiled 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. A large part of the research concentrates on improving the performance of attacks. At the same time, little is done to understand them and, even more importantly, use that knowledge in the design of more secure implementations. In this webinar, 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 assistant professor in the Cybersecurity group at TU Delft, The Netherlands. His research interests are security/cryptography, machine learning, and evolutionary computation. Before the assistant professor position, Stjepan was a postdoctoral researcher at the ALFA group, MIT, USA. Before that, he was a postdoctoral researcher at KU Leuven, Belgium, as a part of the Computer Security and Industrial Cryptography (COSIC) group. Stjepan finished his Ph.D. in 2015 with a topic on cryptology and evolutionary computation techniques. Stjepan also has several years of experience working in industry and government. .